1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches

Abstract

Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs) has led research to improve the efficiency and performance of barcode recognition in Supply Chain Management (SCM). D-CNNs required real-world images embedded with ground truth data, which is often not readily available in the case of SCM barcode recognition. This study introduces two invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied and compared over a set of recently available barcode datasets. To confirm the model’s performance and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU thresholds and image transformation settings. The results show that YOLO v5 works best for the ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are available for developing barcode recognition models and positively affect comparative studies.

Full text

Citation: Kamnardsiri, T.;
Charoenkwan, P.; Malang, C.;
Wudhikarn, R. 1D Barcode Detection:
Novel Benchmark Datasets and
Comprehensive Comparison of Deep
Convolutional Neural Network
Approaches. Sensors 2022,22, 8788.
https://doi.org/10.3390/s22228788
Academic Editor: Gianni D’Angelo
Received: 14 October 2022
Accepted: 10 November 2022
Published: 14 November 2022
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sensors
Article
1D Barcode Detection: Novel Benchmark Datasets and
Comprehensive Comparison of Deep Convolutional Neural
Network Approaches
Teerawat Kamnardsiri 1, Phasit Charoenkwan 2,3 , Chommaphat Malang 4,* and Ratapol Wudhikarn 3,5,*
1Department of Digital Game, College of Arts, Media and Technology, Chiang Mai University,
Chiang Mai 50200, Thailand
2Department of Modern Management and Information Technology, College of Arts, Media and Technology,
Chiang Mai University, Chiang Mai 50200, Thailand
3A Research Group of Modern Management and Information Technology, College of Arts,
Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
4Department of Digital Industry Integration, College of Arts, Media and Technology, Chiang Mai University,
Chiang Mai 50200, Thailand
5Department of Knowledge and Innovation Management, College of Arts, Media and Technology,
Chiang Mai University, Chiang Mai 50200, Thailand
*Correspondence: kanokwan.ma@cmu.ac.th (C.M.); ratapol.w@cmu.ac.th (R.W.);
Tel.: +66-53-920-299 (ext. 416) (R.W.)
Abstract:
Recent advancement in Deep Learning-based Convolutional Neural Networks (D-CNNs)
has led research to improve the efficiency and performance of barcode recognition in Supply Chain
Management (SCM). D-CNNs required real-world images embedded with ground truth data, which
is often not readily available in the case of SCM barcode recognition. This study introduces two
invented barcode datasets: InventBar and ParcelBar. The datasets contain labeled barcode images
with 527 consumer goods and 844 post boxes in the indoor environment. To explore the influential
capability of the datasets that affect recognition process, five existing D-CNN algorithms were applied
and compared over a set of recently available barcode datasets. To confirm the model’s performance
and accuracy, runtime and Mean Average Precision (mAP) were examined based on different IoU
thresholds and image transformation settings. The results show that YOLO v5 works best for the
ParcelBar in terms of speed and accuracy. The situation is different for the InventBar since Faster
R-CNN could allow the model to learn faster with a small drop in accuracy. It is proven that the
proposed datasets can be practically utilized for the mainstream D-CNN frameworks. Both are
available for developing barcode recognition models and positively affect comparative studies.
Keywords:
barcode dataset; deep learning; convolutional neural network; barcode recognition;
barcode detection; benchmarking
1. Introduction
In recent years, deep learning (DL) has been widely accepted and commonly applied
in a variety of study fields more than other machine learning (ML) algorithms [
1
]. DL could
provide outstanding performance in terms of quality, speed, precision, or accuracy across
various applications and research domains. Based on its distinctive advantages and its
practical uses in both real-life and experimental situations, DL has overcome other past
well-known techniques. Thus, it has been highly adopted in several domains, such as com-
munication systems [
2
], manufacturing and production system [
3
], finance [
4
], tourism [
5
],
medical processing [
6
], computer games [
7
], bioinformatics [
8
], robotics [
9
], and so on.
Similar to other research domains, supply chain management (SCM) could substantially
benefit from adopting DL methods in a broad range of SCM parts and activities. Especially
a barcode recognition task, which is identified as a backbone of SCM, could achieve its
Sensors 2022,22, 8788. https://doi.org/10.3390/s22228788 https://www.mdpi.com/journal/sensors

Sensors 2022,22, 8788 2 of 27
goals efficiently and effectively when applying the DL method. DL could improve both
qualities of barcode images with better clearness, as well as fineness [
10
–
12
], and barcode
analysis performance with greater accuracy and real-time performance [13–15].
Regarding the substantial benefits of DL, it has become widespread in the barcode
recognition task in recent years. From past related studies, DL approaches applied to
barcode analysis can be categorized into two major categories: the multi-layer perceptron
(MLP) and convolutional neural networks (CNNs). From these two techniques, CNNs-
based DL, also known as deep CNNs or D-CNNs, is more utilized than the MLP algo-
rithm [
1
]. D-CNNs have outperformed MLP in several dimensions. One of its distinctive
and superior capabilities over MLP is the improvement of information loss originating
from converting two-dimension images to one-dimension signals [
16
]. Therefore, regard-
ing the specific advantage of D-CNNs, this technique has been incorporated into various
barcode recognition tasks, which can be categorized into two primary operations, including
detecting and decoding processes. In the recent decade, several studies applied D-CNNs
with barcode recognition tasks. Nevertheless, all the past attempts still involved two ma-
jor limitations.
The first issue concerns the limited sources of public and realistic barcode datasets.
Generally, in DL model development studies, an efficient dataset is crucial and highly
required. Undeniably, the data used for training the model has substantial effects on the
robustness of the developed DL method [
17
]. Similar to DL models in other fields, the
development of D-CNN-based barcode recognition model requires a reliable, high-quality,
and realistic dataset. Moreover, as our recent study [
18
] pointed out, the DL-based barcode
recognition methods mainly rely on a large and high-quality dataset with ground truth
data. Unfortunately, these barcode datasets are mostly not readily and publicly available for
model training and testing, especially for free use. The public barcode datasets are currently
faced with data annotation problems and are labor-intensive. Most of them are not ready
to be used due to a lack of annotated data and require user manual labeling. Some public
barcode datasets do not combine harsh conditions in real-world environments, causing
biases in model training and barcode decoding. Although the existing private resource
datasets are often generated to resolve all the above issues, accessing is not permitted. From
the above limitations, they restrict scholars, as well as practitioners from accessing various
high-quality and realistic datasets. These circumstances could consecutively obstruct them
from sufficient training and testing and creating efficient DL models.
The latter limitation involves the limited adoption of D-CNN methods, which still
could not cover a wide range of well-known approaches and their recent frameworks.
Therefore, the limited realization of comprehensive D-CNN methods’ performances signifi-
cantly obstructs the ability of scholars and practitioners to perceive the current optimum
methods for the barcode recognition task. To the best of our knowledge [
18
], some well-
known and efficient D-CNN frameworks, such as EfficientDet and RetinaNet, have not
been utilized with the barcode recognition task. However, they were widely and mostly
applied with image recognition tasks in other domains such as medicine, transportation,
agriculture, etc. Furthermore, the adoption of recent and efficient frameworks of D-CNN
methods is still neglected in past studies. For example, one of the most famous object
recognition methods [
19
], You-Only-Look=Once (YOLO), has been widely applied in most
D-CNN-based barcode recognition studies. Nevertheless, until now, the last version of
YOLO, which was deployed to barcode recognition studies, was YOLO version 4 [
14
]
despite the current YOLO version 6. However, YOLOv5 has been claimed as a game
changer for several research domains and industries among the YOLO family [
20
]. It could
bring several advantages and significantly better performance over the past versions, such
as more accuracy [
21
], smaller size [
20
], and faster training [
22
]. Regarding the superior
performance of the recent version and other underexplored D-CNN methods and, sig-
nificantly, none of their application in the barcode recognition task, this important and
critically limits the recognition and future improvement of capabilities of D-CNN-based
barcode recognition.

Sensors 2022,22, 8788 3 of 27
To improve the major limitations of D-CNN-based barcode recognition mentioned
above, in this study, we propose two novel barcode datasets named “InventBar” and
“ParcelBar” for developing and investigating a robust DL-based barcode recognition model.
The first dataset, InventBar, comprises 527 images captured from daily life consumer goods
from supermarkets, and the second dataset, ParcelBar, consists of 844 images of parcels
shot from post offices. As the traditional 1D barcodes are more commonly used and have
long-range impacts in the SCM domain, the proposed datasets only emphasize the 1D
barcodes. The datasets differ from the previous public barcode datasets, which are the
real-life captured barcode images in the SCM domain. InventBar and ParcelBar contain
a sufficient number of barcodes with different sizes of barcode regions and are provided
with data annotations. Inspired by the assumption that real-world barcode images are
not often of a high quality, our proposed datasets were created by involving five distinct
quality distortions, i.e., light conditions, complex backgrounds, rotations, different sizes
of bounding boxes, and blurry areas. These proposed datasets are publicly available
and also made free of charge. The datasets containing the original barcode images and
the respective annotations are available at https://cmu.to/BenchmarkBarcodeDatasets
(created on 13 October 2022).
To perceive and compare the performance of the well-known state-of-the-art D-CNN
architectures, secondly, we benchmark underexplored DL techniques for barcode recog-
nition (i.e., YOLO v5 [
23
], YOLO x [
24
], EfficientDet [
25
], and RetinaNet [
26
]) with other
previously and widely applied D-CNN methods (i.e., Faster Region Convolutional Neural
Network or Faster R-CNN [
27
]). In this aspect, our work contributes to developing an
alternative solution for barcode recognition. We examine the hypothesis that D-CNN-based
barcode recognition algorithms can be optimized both in speed and accuracy for SCM
applications, especially when using a set of well-defined barcode objects. Regarding our
proposed improvements, the contributions of this study can be listed as follows.
1.
Benchmarking recent state-of-the-art and underexplored D-CNN frameworks with
other prior well-known solutions by utilizing the novel barcode datasets: InventBar
and ParcelBar and other former public and realistic datasets.
2.
Analyzing some significant characteristics of the recent publicly available barcode
datasets corresponding with the application effects of the well-known D-CNNs on 1D
barcode detection.
3.
Collecting and maintaining the recent barcode datasets with well-completed annota-
tions and partitioning them into a series of training, validation, and test sets; those are
ready for use.
4.
Evaluating both the performance and efficiency of all implemented D-CNN solutions.
The remaining parts of this study are organized as follows: Section 2provides past
studies on barcode datasets and applications of D-CNNs in barcode recognition. Section 3
describes the materials and methods adopted in this study, followed by the results discussed
in Section 4. Finally, Section 5concludes the research findings, limitations, and possible
future works.
2. Related Works
2.1. Previous Barcode Datasets
Over seven decades ago, different barcode datasets were invented and adopted broadly
in academic and commercial domains. Massive barcode data and the quality of barcode
images have made new barcode recognition methods based on D-CNNs increasingly dom-
inant. Barcode data played a key role in building an intelligent approach for barcode
localization and decoding, while its quality is necessary for the D-CNNs to operate ef-
ficiently. It is undeniable that the more and better the barcode data we provide to the
D-CNNs model, the faster the model can learn and improve.
In the field of SCM, it is common knowledge that the existence of Computer Vision
(CV) methods, i.e., the DL allows substantial improvement and significantly enhances both
ability and performance of barcode recognition and analysis. Several previous pieces of

Sensors 2022,22, 8788 4 of 27
research have thoroughly examined and studied barcode recognition using D-CNN-based
tools and techniques. Some studies also proposed barcode datasets that can be reused
for developing barcode detection and analysis models [
28
–
30
]. Until now, two common
classes of barcode datasets have been developed; those are public and private datasets.
Public barcode datasets are datasets containing either synthetic barcode images or real-
world captured barcode images. They were previously collected by research scholars or
practitioners and made available for public use [
31
]. The private barcode datasets, on the
contrary, are primary source barcode databases with restricted access. Apart from the above
two classes, there is also the synthetic barcode or the computer-generated dataset. This
class of barcode dataset requires less effort to obtain labeled barcode images, which also
benefits the model development.
Current barcode recognition studies require a sufficient number of high-quality
datasets for model training and benchmarking. However, most existing ones are not
given instant access; they are private or unsearchable [
32
–
38
]. The public or online datasets
can be easily accessed and freely utilized among different sources of barcode datasets.
As declared in our previous study [
18
], the public datasets are denoted as the most often
used ones. They receive more remarkable attention from scientific research than private
barcode datasets. Despite the high accessibility, only a few public barcode datasets are
currently available. The statistical evidence from barcode analysis research in 2017–2021
shows the three most frequently utilized barcode datasets, i.e., the well-known Art-Lab,
Art-Lab Rotated, and WWU Muenster (accounting for more than 64%). This result could
emphasize the lack of public barcodes and highlight the necessity of new public barcode
datasets for SCM and related research areas.
To gain a broader perspective of the currently available barcode datasets, in this
section, we give a brief overview of the existing public barcode datasets that play an
important role in this research area. As illustrated in Table 1, there are nine publicly
available barcode datasets, including the Arte-Lab Medium Barcode Dataset (Set1 and
Set2) [
39
], Arte-Lab Rotated Barcode Dataset [
40
], WWU Muenster Dataset [
41
], 1D Barcode
Extended Dataset [
42
], Dubska’ M Dataset [
43
], Sörös G, and Flörkemeier’s Dataset [
29
],
Bodnár-Synthetic, and Bodnár-Huawei Dataset [
30
]. Detailed information about each
dataset is also presented, for instance, the size of the dataset, the number of barcode images
contained in each image, the pixel resolution of the barcode images, and different features
of the barcode images.
Table 1. Current publicly available barcode datasets.
Name Size Resolution
(Pixel)
Instance per
Image Image Feature Barcode Annotation
Single Multiple Synthetic Real-Life Provided No. of
Annotations
Arte-Lab Medium
Barcode (Set 1) 215 640 ×480 3 3
Arte-Lab Medium
Barcode (Set 2) 215 640 ×480 3 3
Arte-Lab Rotated
Barcode 365 640 ×480 3 3 3
1D Barcode Extended 155 648 ×488 3 3 3 3 155
WWU Muenster 1055 640 ×480 3 3 3
DubskáM. 400 604 ×402 3 3 3 400
Sörös G. 320 720 ×1280 3 3 3 328
Bodnár-Synthetic
10,000
512 ×512 3 3
Bodnár-Huawei 98 1600 ×1200 3 3 3
Percentage 100% 33.33% 33.33% 77.78% 33.33%
The same group of researchers invented the first four datasets presented in Table 1.
They are all maintained by the Applied Recognition Technology Laboratory, Department

Sensors 2022,22, 8788 5 of 27
of Theoretical and Applied Science, University of Insubria [
44
]. The Arte-Lab Medium
Barcode Datasets [
39
] can be separated into two sets. Both contain an equal number of
barcode images captured by a Nokia 5800 mobile phone. Barcode images in the Arte-
Lab Medium Barcode (Set 1) are taken from devices with autofocus, whereas Set 2 were
collected using devices without autofocus. Each image contains at most one non-blurred
EAN barcode. However, barcodes that appeared in Set 1 are rotated by, at most,
±
30
◦
from the vertical, enabling this dataset to not be suitable for evaluating the performance
of angle invariant algorithms. Due to the lack of barcode resources and to serve barcode
orientation detection, Zamberletti et al. [
42
] extended the original Arte-Lab dataset to
include a few more barcode images in different rotation angles. The dataset is enclosed
with binary images that allow defining the object region precisely. Another alternative
dataset is the 1D Barcode Extended Dataset [
42
]. It was specifically proposed for evaluating
the detection algorithms in the Hough transform space. The dataset comprises a subset of
barcode images from Arte-Lab or some images captured from the same products presented
in the Arte-Lab Rotated Dataset. Evidently, the barcode images and some characteristic
appearances of all these datasets are identical, proving wholly inadequate. The datasets
could not indeed feed the model with various barcode objects, which might be the biggest
hindrance to the learning process for barcode recognition. Thus, it is required for the new
barcode dataset that fully captures all new barcode images differently.
Apart from the limitation of public data, there are some challenges regarding the
different sizes and dataset quality. The size of the barcode dataset is one of the biggest
concerns for the efficient learning process. The D-CNNs always require a sufficient number
of barcodes to reasonably approximate the unknown underlying mapping function from
the input barcodes. However, as shown in Table 1, some searchable datasets are relatively
small, comprising a hundred or less than hundreds of images (i.e., Bodnár-Huawei) that
are further divided into a small training set and test set. It is worth reminding that in-
sufficient training data will result in a poor approximation (either underfit or overfit the
small training dataset). In contrast, too-small test data fundamentally allow an optimistic
and high variance estimation [
45
,
46
]. To make the D-CNNs training possible, the major-
ity of barcode recognition studies required a heavy data augmentations process [
47
,
48
],
which can provide more representative training samples but consume more time and high
computational complexity.
Another key success of barcode recognition also depends on the quality of barcode
images and their influences on the model performance. In practical applications, the
input images cannot always be assumed to be of high quality [
49
]. In computer vision
applications, high-quality barcodes, e.g., clear backgrounds, simple patterns, and high-
resolution images, do not confirm the recognition method’s performance. At the same time,
barcode recognition in low-quality images is an important capability for D-CNNs. However,
too complicated background, large image size, and a variety of barcode appearances might
also bring D-CNNs learning and decoding tasks into a highly challenging procedure [11].
Regarding model training, there might appear to be a trade-off between the quality
of datasets and model performance. As stated in [
50
], high image resolution for D-CNNs
training directly affects the maximum possible batch size, causing a delay and high com-
putational consumption. Moreover, a simple barcode image with a clear background or a
large area of barcode objects might provide better accuracy but cause more overfitting [
48
].
Thanks to the research improvement in this area, as can be observed in Table 1, various
existing barcode datasets are more focused on barcodes with specific features taken from
real-life, most of which are imperfect or low-quality images. This way, the optimal selection
of datasets containing different image features might significantly benefit the training and
testing process rather than the high-quality images.
In addition, barcodes in some datasets, i.e., DubskáM., Bodnár-Synthetic, and Bodnár-
Huawei, are not real-world samples, and the representation of barcodes does not even
include real-life conditions. As seen in [
51
], their experiment was done over the Bodnár-
Huawei dataset. The dataset contains computer-generated barcodes overlaid on the real

Sensors 2022,22, 8788 6 of 27
background images instead of the fully captured real-world barcode images. This circum-
stance could also limit the capability of the D-CNN-based barcode recognition algorithms
since the model has less opportunity to learn and improve from various distinct condi-
tions of barcodes. When the datasets were applied to more specific analytical purposes,
barcode recognition algorithms might fail to consider real-world characteristics and harsh
conditions. Although many D-CNN methods have obtained state-of-the-art performance
and can deal with the barcodes in different angles, shapes, and image quality, the methods
might provide precise results at the experimental level but not the practical level.
It should also be carefully considered that the fully captured barcode image datasets are
sometimes generated by adding adversarial objects, conflicted noises, or quality distortions
from artifacts. These sources of noise are imperceptible to human observers, known as
“worst noise” [
49
], which is the cause of deep learning networks misclassified [
52
]. In
the same way, D-CNN may face difficulty predicting the correct class of barcode images
under the worst noise. Encountering well-chosen noises while avoiding the worst noise is
unlikely to be seen in the practical application and has become an interesting problem in the
most recent research. We argue that the choice of barcode dataset containing well-captured
images and some naturally quality distortions, e.g., illuminated, skewed, small, obscured,
blurred, and rotated barcodes are preferable. It is a practical solution for developing the
barcode recognition model that best fits the real-world situation.
Although real-life barcode images have gained more attention in the current public
datasets, 66.67% of the freely available barcodes are labor-intensive because ground truth
data is unavailable. The WWU Muenster dataset is one of the high-quality datasets since it
was established under the actual situation and contains a sufficient number of train and
test images. However, the dataset still required manual labeling by workers to complete
the annotation task.
As a matter of fact, research in this area constantly needs more new images and a large
enough barcode dataset that can efficiently enhance the model development process. A
large margin of real-world barcode datasets should promote the accuracy and performance
of D-CNN-based on barcode recognition. Considering the barcode images with real situa-
tions collected from the actual SCM environment, together with the well-chosen distortions,
are the most necessary. With the support of our proposed datasets, we anticipated that
the D-CNN-based barcode recognition technology could provide significant progress for
detecting and decoding functions.
2.2. Deep Learning (DL) and Convolutional Neural Network (CNNs) for Barcode Recognition
Deep learning (DL) has come to be known as deep structured learning. The DL tech-
nique is considered a specific subfield of machine learning (ML) endowed with artificial
neural networks (ANNs) to enable machines to make accurate decisions without relying
on human supervision [
53
]. DL has attracted great attention in recent research, because it
can efficiently resolve real-life problems and present great promise as a practical solution
in several domains or situations. In computer vision domains, DL has been reported to
outperform traditional approaches in object segmentation, image classification, and recog-
nition [
54
]. Additionally, the advantage of DL could be extended to biological domain [
55
],
computer games [
56
], communication systems [
57
], mobile traffic classification [
58
], and
IoT-based UAV systems [59], as well as the named entity recognition [60].
Among various research fields, barcode recognition is one of the significant domains
adopting DL and can receive better advantages than the traditional approaches. Several
proposed and applied DL architectures can be classified into two primary techniques: multi-
layer perceptron (MLP) and convolutional neural networks (CNNs), known as Deep CNNs
or D-CNNs. However, from these two methods, D-CNNs are identified as more utilized
DL algorithms [
1
] in the barcode analysis, since they can better resolve the information
loss emerging from the conversion of two-dimensional images to one-dimensional vectors
than the MLP architecture [
16
]. Moreover, D-CNNs also could better deal with other
critical issues of barcode recognition and analysis, such as image blurring and image

Sensors 2022,22, 8788 7 of 27
distortion [
11
,
61
]. Therefore, regarding the distinctive advantages of D-CNNs and the
advancement of hardware, several studies have adopted this approach in recent years.
Table 2summarizes studies that apply D-CNN-based on barcode recognition methods in
the barcode recognition field.
Table 2. D-CNN-based barcode recognition methods employed over 2015–2021.
Authors Year D-CNN Public Dataset Private Dataset Accuracy
Chou et al. [61] 2015 CNN CypherLab 0.952
Grzeszick et al. [34] 2016 CNN Product on the racks 0.470
Li et al. [62] 2017 Faster R-CNN
ArteLab
WWU
Muenster
0.989
0.994
Hansen et al. [33] 2017 YOLO v2
ArteLab Rotated
WWU
Muenster
Dubska’ M.
Sörös G.
0.914 (all)
Zhang et al. [63] 2018 SSD Medical Label 0.945
Tian et al. [64] 2018 R-CNN
ArteLab
WWU
Muenster
Mixed Barcode
0.963
(ArteLab and
Muenster)
0.925
Ventsov and Podkolzina [65] 2018 CNN Ventsov N.N 0.974
Zhao et al. [66] 2018 CNN Barcode-30k 0.942
Ren and Liu [67] 2019 SSD
ArteLab
WWU
Muenster
CipherLab
0.885
0.884
0.992
Yang et al. [68] 2019 CNN Fashion Label 0.967
Xiao and Ming [69] 2019 YOLO v2 ArteLabWWU
Muenster
0.912
0.939
Pu et al. [11] 2019 CNN Production line 0.991
Zhang et al. [70] 2019 Fast R-CNN
ArteLabWWU
Muenster
Dubska´M.
Sörös G.
0.871 (all)
Blanger and Hirata [71] 2019 SSD Blanger L. 0.770
Yuan et al. [72] 2019 R-CNN CipherLab
COCO Val2017
UAV123
Yuan, B. 0.999 (all)
Li et al. [73] 2019 DSC DPM Code
QR Code Images 0.999 (all)
Suh et al. [35] 2019 YOLO v2 ArteLab Rotated
WWU Muenster
15 Carriers Shipping
Labels 0.980 (all)
Kalinov et al. [32] 2020 CNN UAV barcode 0.961
Brylka et al. [14] 2020 YOLO v3
ArteLab
ArteLab Roated
WWU Muenster
0.870 (both
ArteLabs)
0.860
Jia et al. [51] 2020 Faster R-CNN
ArteLab
WWU Muenster
Dubska´M.
Sörös G.
Bodnár-Synthetic
Jia, J. 0.834 (all)
Zhang et al. [74] 2020 Fast R-CNN
ArteLab
WWU Muenster
Dubska´M.
Sörös G
Zhang, J. 0.879 (all)
Tan [36] 2020 CNN Logistic Robot Barcode 0.988
Zharkov et al. [75] 2020 CNN ZVZ-Synth
ZVZ-Real 0.967 (all)
Suh et al. [37] 2021 CNN Shipping Labels 0.997
Do and Pham [38] 2021 YOLO v3 COCO Val2017 Supermarket
Products 0.900 (all)
Zhang et al. [15] 2021 YOLO v4 Liwei Z. 0.906
Remark: Convolutional Neural Network (CNN), Region-based Convolutional Neural Network (R-CNN), Single-
Shot Detector (SSD), Depth-wise Separable Convolution (DSC), and You-Only-Look-Once (YOLO).
From Table 2, it can be seen that the main D-CNN methods that were employed over
2015–2021’s barcode studies include CNNs, SSD, R-CNN, Fast R-CNN, Faster R-CNN, DSC,

Sensors 2022,22, 8788 8 of 27
and different versions of YOLO, ranging from version 2 (v2) to version 4 (v4). These DL
methods can be classified into two major categories of object detectors; multiple-stage and
single-stage detectors [
76
]. The multiple-stage method, mainly two-stage detectors, such
as CNNs, R-CNN, Fast R-CNN, and Faster R-CNN, generates regions of interest before
defining candidate bounding boxes. On the other hand, single-stage detectors, such as
YOLO, and SSD, execute bounding-box regression and object classification simultaneously.
Regarding their distinctiveness, typically, the multiple-stage detectors can reach higher
localization and accuracy rates, while their speed is lower than the single-stage detectors.
From the different applications of D-CNN methods in the barcode recognition study
presented in Table 2, CNN was the most frequently applied to this topic (10 out of 26 pa-
pers). Conversely, YOLO was denoted as the second most used technique (six papers).
Nevertheless, as our past study [
18
] indicated, the analysis result shows a significant drop
in D-CNNs utilization during 2020–2021 compared to the previous periods between 2015
and 2019. Especially considering the proportion of each popular method applied between
the most recent year (2021) and whole years (2015–2021), YOLO was utilized more than
30%, while CNN adopted only 10%. The significant decline of CNN attention and applica-
tion mainly comes from the fundamental issues of multiple-stage detectors, especially the
more complex process and low-speed detection rate that do not meet both actual industrial
requirements and real-life usages [77,78].
On the other hand, when focusing on 2021, YOLO architecture was the most applied
method, taking more than 66% of articles related to the barcode recognition and analysis
tasks. This declaration also emphasizes the single-stage detector in the barcode recognition
task. Nevertheless, to the best of our knowledge, several approaches to single-stage
detectors are recently, widely, and continuously adopted. Until now, some of the latest
approaches, such as EfficientDet (popular in the biological domain), RetinaNet (widely used
for detecting objects in aerial and satellite imagery), and the earliest version of the existing
YOLO, are mostly claimed for better performance but have still never been explored in the
barcode recognition research.
Therefore, regarding the limitation of applying modern and widely acknowledged
approaches of D-CNNs, in this study, we adopt five representative object detection-based
D-CNN methods, including the prior well-known and distinctive SCM solutions, i.e., Faster
R-CNN [
27
] and a set of underexplored methods, which are EfficientDet [
25
], RetinaNet [
26
],
YOLO v5 [
23
], and YOLO x [
24
], to comprehensively perceive and benchmark the effective-
ness and efficiency of various D-CNN approaches.
3. Materials and Methods
3.1. Experimental Settings
This section introduces an outline process and methodologies used in this study.
There are three key processes: data annotations, transfer learning, and model training
and testing. A detailed explanation for each process will also be described. We applied
five D-CNN-based methods and investigated key characteristics and quality of seven
benchmark barcode datasets using different evaluation metrics. We used a Windows 10 OS
laptop computer equipped with Intel(R) Core(TM) i5-8265U CPU@1.60 GHz, 2 GB NVIDIA
graphic card, and 8 GB DDR4 RAM (ASUSTek Computer Inc., Taipei, Taiwan) for exploring,
prototyping, and tuning hyper-parameter. The model training and testing were performed
on the Kaggle web-based data-science environment (https://www.kaggle.com/) that offers
a P100 GPU with 16 GB memory on Intel(R) Xeon(R) CPU@2.30 GHz model (accessed on
13 April 2022).
3.2. Dataset Description
As described in the previous section, few datasets deal with detecting barcodes in a
specific SCM domain. Zamberletti et al. [
39
] presented the Medium Barcode 1D Collection,
known as the Arte-Lab Barcode Dataset, which contains only book barcode images. In line
with this, the Arte-Lab Rotated Barcode Dataset has been proposed as an extension. The

Sensors 2022,22, 8788 9 of 27
new version of Arte-Lab contains rotated book barcodes from different angles and com-
prises a few barcodes from daily life products. Although the 1D Barcode Extended Dataset
contains consumer-packaged goods barcodes [
42
], their provided barcode objects are not
varied, most of which are images taken from a single consumer good with distinctive posi-
tions. Additionally, there is the dataset proposed for deblurring algorithms [
29
]. The dataset
comprises blurry barcode images captured with intentions, thus far from everyday images.
Some other existing datasets, such as Bodnár [
30
] and DubskáM. [
43
], encompassed the
computer QR codes on both artifacts and real-world background images. Dissimilar to the
WWU Muenster dataset [
41
], which is more probably to provide a high-feature representa-
tion of SCM objects with real scenarios. Obviously, most of the existing datasets show no
sign of real-life SCM barcode objects captured from a variety of products. Moreover, none of
the abovementioned datasets offer a comprehensive range of barcode tags on parcels from
the express delivery service. These matters might limit building computational solutions
for barcode analysis and recognition in the daily SCM environment.
To uncover the issues above, we present two new barcode datasets, the InventBar
dataset, and the ParcelBar dataset. The main purpose of giving these two datasets is
to provide a new set of barcode images with the presence of real or natural conditions
that could also benefit the SCM and computer science communities. These two barcode
recognition datasets specifically deal with SCM-related objects in the presence of indoor
scenes. In the data collection process, all barcode images were collected manually using
Samsung Galaxy S10 Plus with a 16 MP (f/2.2) ultrawide camera. We easily capture all
barcode images within a short distance, ranging from an inch to a few feet. Barcode images
with complex natural backgrounds, skews, blurry regions, and lighting conditions were
grabbed, representing the most common real-world features. This operation would allow
the model to deal with a higher challenge in barcode quality but prove the strengths of
D-CNNs in 1D barcode recognition. We hoped that both InventBar and ParcelBar could
serve as the basis for the D-CNN-based barcode detection and decoding approaches that
can support further research on daily life barcodes in SCM.
3.3. Data Annotations
Inventing a new barcode dataset required the most expensive steps to manually label
all collected barcode images by annotators [
79
]. The data-labeling process aims to provide
a bounding box for the barcode in each photograph. Our InventBar and ParcelBar are one-
class labeled datasets where all data corresponds to the axis points of the barcode region.
The formerly proposed dataset, InventBar, is a collection of unique product identifiers
ready to be sold in grocery stores. All barcode images are positives containing 1D barcodes
with purely unique numbers. The latter dataset, ParcelBar, contains post-box tags collected
from the indoor logistic warehouse. All datasets contain images captured from mobile
cameras; thus, each image encloses either one or several barcode tags.
Before annotating the data, we performed a data cleaning process over raw datasets
by removing duplicated images containing exactly the same instances captured at a similar
angle. In our case, the duplicated barcode images are unintentionally taken from the
burst mode. This preprocessing step resulted in 527 images of the InventBar dataset with
relatively high quality (4032
×
3024 pixels), whereas ParcelBar involves 844 images in
originally 1478
×
1108-pixel dimensions. There are 527 and 1088 barcode instances for
InventBar and ParcelBar, respectively.
After that, we used the open-source software LebelImg V1.8.0 (https://sourceforge.
net/projects/labelimg.mirror/files/v1.8.0/, accessed on 13 October 2022) to annotate all
original barcode samples. The barcode instances are covered with the rectangular bounding
box corresponding to four fundamental values, including x1, y1, x2, and y2, where x1 and
y1 indicate the upper-left corner of the bounding box. It is noticeable that the data-labeling
process significantly affects the level of detection accuracy. With a small mistake on the
data label, the D-CNN models cannot effectively learn the ground truth, leading to fault
detection. To ensure a high-quality annotation, two additional machine learning and deep

Sensors 2022,22, 8788 10 of 27
learning practitioners participated in cross-checking and verifying the correctness of the
barcode labels. In this regard, mislabeled barcode instances should also be reported and
adjusted promptly.
We investigated the barcode tags based on the wrapped bounding box area for a
detailed analysis of the significant features of barcode datasets. Based on the COCO 2017
dataset [
80
], barcodes in all images can be classified into small, medium, and large bounding
box regions. As can be observed in Table 3, InventBar and ParcelBar show a greater
proportion of large-sized barcode tags (accounted for 86.14% and 67.28%, respectively). In
comparison, only 26.56% of the overall barcode instances are considered medium. It is also
clear that both datasets show no sign of the small-sized barcodes.
Table 3. Number of different-sized barcode regions contained in InventBar and ParcelBar.
Dataset No. of
Images
No. Barcode Regions in Different Sizes No. of
Annotations
Small Medium Large
InventBar 527 0 73 454 527
ParcelBar 844 0 356 732 1088
In accordance with the illustrations shown in Figure 1, our datasets not only present
a barcode region in different scales but also involves diverse background texture from
natural scenes or real-world SCM environments, such as the ground floor, products on the
shelves, plain post boxes, and striped boxes with rope and messy characters. These key
features make our proposed datasets complete and most suitable for training the barcode
recognition algorithms.
3.4. Transfer Learning
After manually labeling the barcode datasets, transfer learning was utilized to fine-
tune D-CNN-based barcode recognition models to realize accurate detection of barcode
objects [
77
] and to accelerate the training time of all comparative models. It is a helpful
technique that allows D-CNN-based methods to learn from a limited amount of data [
81
]
but can still achieve a better result and with more computationally efficient [
82
]. By
applying transfer learning in this study, the adopted D-CNN methods can perform a new
task (detecting barcode objects) based on the knowledge from the previous well-trained
models in different but related problems [
83
]. Accordingly, we used an IceVision pretrained
framework over a large-scale object detection dataset, namely MS COCO 2017 (Microsoft
Common Objects in COntext) [
80
], using different backbones shown in Table 4. The dataset
comprises various image classes, such as persons, cars, and animals, with annotations for
object attributes.
Table 4. Pretrained backbone network architectures used for D-CNN methods.
Authors D-CNN Methods Backbone
Tan et al., 2020 [25] EfficientDet tf_lite0
Ren et al., 2016 [27] Faster R-CNN resnet50_fpn_1x
Lin et al., 2018 [26] RetinaNet resnet50_fpn_1x
ultralytics/yolov5, 2022 [23] YOLO v5 small
Ge et al., 2021 [24] YOLO x yolox_s_8x8

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Sensors 2022, 22, x FOR PEER REVIEW 11 of 28
dataset [80], barcodes in all images can be classified into small, medium, and large
bounding box regions. As can be observed in Table 3, InventBar and ParcelBar show a
greater proportion of large-sized barcode tags (accounted for 86.14% and 67.28%, respec-
tively). In comparison, only 26.56% of the overall barcode instances are considered me-
dium. It is also clear that both datasets show no sign of the small-sized barcodes.
Table 3. Number of different-sized barcode regions contained in InventBar and ParcelBar.
Dataset No. of Images
No. Barcode Regions in Different
Sizes No. of Annotations
Small Medium Large
InventBar 527 0 73 454 527
ParcelBar 844 0 356 732 1088
In accordance with the illustrations shown in Figure 1, our datasets not only present
a barcode region in different scales but also involves diverse background texture from
natural scenes or real-world SCM environments, such as the ground floor, products on
the shelves, plain post boxes, and striped boxes with rope and messy characters. These
key features make our proposed datasets complete and most suitable for training the
barcode recognition algorithms.
(a)
Natural
back-
ground
(b)
Lighting
condition
(c)
Rotation
(d)
Barcode
size
(e)
Blurry
area
InventBar ParcelBar
Figure 1.
Example barcode images from the InventBar and ParcelBar datasets with distinctive natural
characteristics: (
a
) natural background, (
b
) lighting conditions, (
c
) rotation, (
d
) barcode size, and
(e) blurry area.
3.5. Model Training and Testing
In the training process, we have trained and tested five D-CNN network models over
a set of benchmarking datasets. This process also includes barcode data for InventBar and
ParcelBar, as given in an example in Figure 2. The representative D-CNN methods can
be classified into two groups. The mainstream group methods were previously applied
in barcode recognition or one of the SCM solutions, including Faster R-CNN and the
YOLO family. The methods in the second group are taken across the study domain, most
of which are used in a field of biology, i.e., RetinaNet and EfficientDet. D-CNN-based
barcode recognition models were trained and tested on different augmentation modes,
including resizing, horizontal and vertical flip, shift scale rotation, RGB shift, and random
brightness. All models either learned or verified on the well-defined transformation loop,
ranging from 0 (without augmentation), 5, 10, and 20 (the highest augmentation value). For
each comparable dataset, we randomly divided the total number of training samples into
different batches but with the same size (eight samples/batch). The epoch numbers are set
to 25, 50, and 100 to observe the data diversity and the iterative process’s impact, while
the discriminator network’s learning rate is set to 0.001. We set up the rest of the required

Sensors 2022,22, 8788 12 of 27
parameters in each algorithm to their default values of the networks. The trained models
were further tuned for the highest precision and recall rate, which varies between 0 and 1,
using the validation set. Table 5summarizes the general information on all benchmarked
datasets split into three subsets (i.e., training, validation, and testing sets with ratio 40:40:20)
using random selection.
Sensors 2022, 22, x FOR PEER REVIEW 13 of 28
2 Arte-Lab Medium Barcode (Set 2) 86 86 43 215
3 Arte-Lab Rotated Barcode 146 146 73 365
4 WWU Muenster 422 422 211 1055
5 1D Barcode Extended 62 62 31 155
6 InventBar 337 338 169 844
7 ParcelBar 210 211 106 527
Theoretically, the number of samples and image resolution of different barcode da-
tasets significantly affect model training. When the number of barcode images is too
large with a high pixel degree, it could impair the performance of D-CNN-based bar-
code detection. It is worth noting that in common object detection algorithms, different
images vary in lengths and widths. Moreover, the D-CNN-based feature extractions
usually require a square input resolution [84]. Accordingly, uniformly scaling the origi-
nal image to a standard size is needed before feeding them to the prediction network
[20]. We have created a collection of basic datasets by resizing all images into rectangles
with a height and width of 416 and 416, respectively. Thus, all selected D-CNN-based
methods were trained in the 416×416 pixels versions but not in the original resolution.
Note that there is an exception to the smallest images required by EfficientDet that was
restricted to 512 × 512 pixels.
Figure 2. Image of the training data corresponds to the InventBar and ParcelBar and their respec-
tive annotations.
Figure 2.
Image of the training data corresponds to the InventBar and ParcelBar and their respec-
tive annotations.
Table 5. General information of the benchmarked datasets and sub-datasets.
No. Dataset Training
Set
Validation
Set
Test
Set Total
1 Arte-Lab Medium Barcode (Set 1) 86 86 43 215
2 Arte-Lab Medium Barcode (Set 2) 86 86 43 215
3 Arte-Lab Rotated Barcode 146 146 73 365
4 WWU Muenster 422 422 211 1055
5 1D Barcode Extended 62 62 31 155
6 InventBar 337 338 169 844
7 ParcelBar 210 211 106 527
Theoretically, the number of samples and image resolution of different barcode datasets
significantly affect model training. When the number of barcode images is too large with a
high pixel degree, it could impair the performance of D-CNN-based barcode detection. It is

Sensors 2022,22, 8788 13 of 27
worth noting that in common object detection algorithms, different images vary in lengths
and widths. Moreover, the D-CNN-based feature extractions usually require a square input
resolution [
84
]. Accordingly, uniformly scaling the original image to a standard size is
needed before feeding them to the prediction network [
20
]. We have created a collection of
basic datasets by resizing all images into rectangles with a height and width of 416 and 416,
respectively. Thus, all selected D-CNN-based methods were trained in the 416
×
416 pixels
versions but not in the original resolution. Note that there is an exception to the smallest
images required by EfficientDet that was restricted to 512 ×512 pixels.
3.6. Evaluation Methodologies
Based on past studies of DL-based barcode recognition, several common performance
metrics were used to ensure the accuracy and performance of DL methods. In this study, the
detection accuracy of all D-CNN methods was investigated using Mean Average Precision
(mAP). In addition, the runtime is used to evaluate and confirm the influence speed of the
models. The definition and principle of the key evaluation metrics are given as follows:
Mean average precision (mAP) is often used as a standard metric to evaluate the
accuracy and robustness of DL methods in object detection tasks. It can be calculated
according to the Average Precision (AP) of different classes and then averaged over a
number of classes [
85
]. As shown in Equation (2), AP is obtained by measuring pairs of
precision (P) and recall (R) values for different ranks [32].
AP =∑n(Rn−Rn−1)Pn, (1)
mAP =1
N∑N
nAPn, (2)
In this aspect,
P
is the fraction of barcodes correctly recognized by the D-CNN models
over the actual number of all barcodes that the model can recognize. However, R represents
the probability of accurately detecting ground truth barcode images. Hence,
mAP
can be
further calculated by Equation (2), resulting in the possible value from 0 to 1. The highest
mAP score, the most accurate the model is in its detection.
For the comprehensive study, the IoU has also been explored for all experimental
scenarios. IoU is a quantitative measure to quantify how the ground truth and predicted
boxes match. It can be defined as the ratio of Area of Overlap (represents the interaction of
the true ground box and the bounding box of the regression result) to the Area of Union
(represents the union of the truth box and the bounding box of the regression result) [
86
].
Specifically, IoU is used as a threshold to classify whether the prediction is true positive or
false positive [
21
]. The performance of the D-CNN methods in this study was investigated
and compared across different IoU thresholds. This technique avoids the ambiguity of
choosing the optimal IoU threshold for evaluating the accuracy of the competitive models.
The definition of IoU is denoted in (3).
IoU =Area of Overlap
Area of Union , (3)
The
IoU
is equal to 0 means 0% overlap between the predicted and the ground truth
box. Whenever the IoU is 1, there is an exact match between the two boxes. Thus, the
higher the IoU, the better the prediction.
4. Results and Discussion
4.1. Dataset Statistics
Following that, we analyze the key properties of the InventBar and ParcelBar datasets
compared to all benchmarking barcode datasets. Figure 3reveals the fraction of annotated
barcode instances in each dataset. We observed that each of the benchmarking datasets
varies significantly in size (number of images contained in the dataset) and differed in the
number of barcode instances, falling within the small, medium, or large categories. There

Sensors 2022,22, 8788 14 of 27
are no existing small-sized barcodes for all datasets, while the medium-sized barcodes
appeared very few (only two to three instances) in WWU Muenster and Arte-Lab (Set2),
respectively. Simply said that the number of images in the series of Arte-Lab datasets, 1D
Barcode Extended, and WWU Muenster is at the same level as their annotated barcodes.
This means almost all images in the datasets, as mentioned earlier, contained only a single
barcode. In contrast, our new datasets include the captured images with either one barcode
tag or multiple barcode tags, which leads the D-CNNs to enhance their detection capabilities
for similar objects located in the same image. We emphasized that the multiple barcode
instances per image will be useful for training complex D-CNN methods to detect barcodes
more precisely.
Sensors 2022, 22, x FOR PEER REVIEW 15 of 28
emphasized that the multiple barcode instances per image will be useful for training
complex D-CNN methods to detect barcodes more precisely.
It is common knowledge that all object detection algorithms would perform well on
large objects, especially in the event that the models were previously trained on larger
objects [87]. Smaller objects are typically harder to localize and require higher contextual
reasoning to recognize. Similar to our case, all the adopted D-CNNs were pretrained us-
ing an MS COCO dataset encompassing 640 × 480 pixels images [80], while the training
and testing on the real barcode data have been done over 416 × 416 pixels images. As
seen in Figure 3, the InventBar and ParcelBar datasets contain loads of barcode instances
classified as medium-sized, while all barcodes from other datasets are considered large
barcodes. Therefore, it is unsurprising that all D-CNN methods applied over both da-
tasets show comparatively lower detection accuracy because the models prefer larger
barcodes. In this aspect, we can conclude that our proposed datasets contribute some
distinguishing characteristics that could not be observed in other existing datasets. In-
ventBar and ParcelBar were created by addressing one of the critical challenges of object
detection algorithms with various sizes of barcode objects over the real-world fore-
ground and background images.
Figure 3. Number of annotated barcode instances classified by barcode size: small, medium, and
large.
4.2. Barcode Recognition Accuracy
In order to verify the quality of barcode datasets, this paper compares five different
D-CNN algorithms over seven competitive datasets with an image resolution of 416 ×
416 pixels. In-depth analysis of the barcode recognition accuracy, the mAP was evaluat-
ed by considering the overlapping percentage between the ground truth barcode region
and the prediction boundary boxes of barcode. In this regard, recognition accuracy
would reflect the degree to which the D-CNN methods can correctly detect or localize
one or more barcode instances that appeared in the image. The higher the accuracy rate,
the better performance of the detection solution. At the same time, we use IoU threshold
values to indicate different levels of detection confidence. First, we quantify the mAP at
the IoU threshold = 0.5, denoted as mAP@0.5 (i.e., there is only a 50% overlap between
the two regions). Straightforwardly, if the prediction boundary captured over 50% over-
lap with the ground truth barcode region, the prediction was considered a successful
match. For the more challenging detection task, secondly, we set the detection confi-
dence of all comparative models ranging from 0.5 to 0.95, indicated mAP@(0.5–0.95) (i.e.,
considering 50%–90% overlap between the predicted and the actual barcode region) by
increasing every 0.05 and reporting an averaged result.
In Table 6, we collected and summarized the best recognition accuracy of different
D-CNN methods. The D-CNNs were applied over the two proposed datasets and sever-
al other popular datasets, including Arte-Lab Medium Barcode (Set 1), Arte-Lab Medi-
um Barcode (Set 2), Arte-Lab Rotated Barcode Dataset, 1D Barcode Extended Dataset,
Figure 3.
Number of annotated barcode instances classified by barcode size: small, medium,
and large.
It is common knowledge that all object detection algorithms would perform well on
large objects, especially in the event that the models were previously trained on larger
objects [
87
]. Smaller objects are typically harder to localize and require higher contextual
reasoning to recognize. Similar to our case, all the adopted D-CNNs were pretrained using
an MS COCO dataset encompassing 640
×
480 pixels images [
80
], while the training and
testing on the real barcode data have been done over 416
×
416 pixels images. As seen in
Figure 3, the InventBar and ParcelBar datasets contain loads of barcode instances classified
as medium-sized, while all barcodes from other datasets are considered large barcodes.
Therefore, it is unsurprising that all D-CNN methods applied over both datasets show
comparatively lower detection accuracy because the models prefer larger barcodes. In
this aspect, we can conclude that our proposed datasets contribute some distinguishing
characteristics that could not be observed in other existing datasets. InventBar and ParcelBar
were created by addressing one of the critical challenges of object detection algorithms with
various sizes of barcode objects over the real-world foreground and background images.
4.2. Barcode Recognition Accuracy
In order to verify the quality of barcode datasets, this paper compares five differ-
ent D-CNN algorithms over seven competitive datasets with an image resolution of
416 ×416 pixels
. In-depth analysis of the barcode recognition accuracy, the mAP was
evaluated by considering the overlapping percentage between the ground truth barcode
region and the prediction boundary boxes of barcode. In this regard, recognition accuracy
would reflect the degree to which the D-CNN methods can correctly detect or localize
one or more barcode instances that appeared in the image. The higher the accuracy rate,
the better performance of the detection solution. At the same time, we use IoU threshold
values to indicate different levels of detection confidence. First, we quantify the mAP at
the IoU threshold = 0.5, denoted as mAP@0.5 (i.e., there is only a 50% overlap between
the two regions). Straightforwardly, if the prediction boundary captured over 50% overlap
with the ground truth barcode region, the prediction was considered a successful match.
For the more challenging detection task, secondly, we set the detection confidence of all

Sensors 2022,22, 8788 15 of 27
comparative models ranging from 0.5 to 0.95, indicated mAP@(0.5–0.95) (i.e., considering
50%–90% overlap between the predicted and the actual barcode region) by increasing every
0.05 and reporting an averaged result.
In Table 6, we collected and summarized the best recognition accuracy of different
D-CNN methods. The D-CNNs were applied over the two proposed datasets and several
other popular datasets, including Arte-Lab Medium Barcode (Set 1), Arte-Lab Medium
Barcode (Set 2), Arte-Lab Rotated Barcode Dataset, 1D Barcode Extended Dataset, and
WWU Muenster. Compared to other D-CNN methods and with mAP@(0.5–0.95), YOLO v5
presents a higher mAP for all benchmarked datasets. These results show that the YOLO
v5 can detect barcode objects more accurately. It can also imply that YOLO v5 is the most
robust model in the SCM domain since it provides a good result even measured with a high
degree of matching confidence. The tendency of mAP measured in all datasets is obviously
in the same direction. Leastwise, the results obtained from the two invented datasets do
not deviate from the comparative ones.
Table 6.
The best barcode detection accuracy of different D-CNN methods applied over all bench-
marked datasets.
D-CNN-
Based
Methods
Arte-Lab
(Set 2)
Arte-Lab
(Set 1)
Arte-Lab
Rotated
WWU
Muenster
1D
Barcode
Extended
InventBar ParcelBar
mAP
0.5
mAP
0.5–0.95
mAP
0.5
mAP
0.5–0.95
mAP
0.5
mAP
0.5–0.9
mAP
0.5
mAP
0.5–0.9
mAP
0.5
mAP
0.5–0.9
mAP
0.5
mAP
0.5–0.9
mAP
0.5
mAP
0.5–0.9
EfficientDet 1.000 0.881 1.000 0.857 1.000 0.855 0.999 0.782 1.000 0.854 0.954 0.758 0.991 0.855
Faster R-CNN 1.000 0.882 1.000 0.861 1.000 0.859 1.000 0.792 1.000 0.880 0.997 0.827 0.985 0.854
RetinaNet 1.000 0.884 1.000 0.840 1.000 0.876 1.000 0.809 1.000 0.869 0.994 0.812 0.994 0.851
YOLO v5 0.998 0.936 0.998 0.904 0.996 0.935 0.998 0.896 0.998 0.930 0.996 0.873 0.994 0.918
YOLO x 1.000 0.833 1.000 0.827 0.970 0.848 1.000 0.813 0.996 0.726 0.998 0.810 0.981 0.856
Conversely, when a 50 percent overlap between the predicted and the actual barcode
is considered, the mAP of both YOLO v5 and YOLO x displayed the lowest value for
almost all datasets except InventBar. The reason is that YOLOs perform a greater number
of detection errors than the existing D-CNN methods. In addition, the YOLOs network
often struggles to detect small and adjacent objects from each grid with only two bounding
box regions [
88
]. Interestingly, when the D-CNN models were applied over the two
proposed datasets (InventBar and ParcelBar), none of the models reached 1.0 mAP. On
the other hand, D-CNNs applied on the remaining datasets do have. The characteristics
of the benchmarking datasets apparently biased the model training to detect barcodes,
particularly at IoU 0.5 easily. This means that the model acknowledges the perfect match at
only half of a barcode tag is detected. Either at IoU 0.5 or IoU 0.5–0.95, however, the mAP
results tested on InventBar and ParcelBar are more reasonable. This evidence proves that
our datasets are scene-based and exhibit unique characteristics that brought all adopted
models to fall into a higher challenge than the other datasets.
To observe the detailed characteristics of different D-CNN methods over seven bench-
marked datasets, we conducted the training process by taking advantage of different exper-
imental configurations. Figures 4–8demonstrated the barcode recognition rate (mAP@0.5
and mAP@(0.5–0.95)) of EfficientDet, RetinaNet, Faster R-CNN, YOLO v5, and YOLO x,
respectively. In corresponding to what has been described in Section 3.5, we also quantified
and reported the mAP results based on the augmentation degree. For each set of illus-
trations, the mAP values from the two IoU thresholds were calculated at different epoch
intervals, i.e., 25, 50, and 100, shown as follows.

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Sensors 2022, 22, x FOR PEER REVIEW 17 of 28
observed in Figures 4–8 (d). On different image-augmented distributions, the detection
accuracy observed in InventBar and ParcelBar is nearly stabilized. Their mAP variations
were very small when tested on a large number of epochs with intensively augmenting
the images, except only in the case of YOLO x, which shows massive fluctuations.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 4. Recognition rate of EfficientDet applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 5. Recognition rate of Faster R-CNN applied over seven public barcode datasets.
Figure 4. Recognition rate of EfficientDet applied over seven public barcode datasets.
Sensors 2022, 22, x FOR PEER REVIEW 17 of 28
observed in Figures 4–8 (d). On different image-augmented distributions, the detection
accuracy observed in InventBar and ParcelBar is nearly stabilized. Their mAP variations
were very small when tested on a large number of epochs with intensively augmenting
the images, except only in the case of YOLO x, which shows massive fluctuations.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 4. Recognition rate of EfficientDet applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 5. Recognition rate of Faster R-CNN applied over seven public barcode datasets.
Figure 5. Recognition rate of Faster R-CNN applied over seven public barcode datasets.
Considering all experimental scenarios illustrated in Figures 4–8, the best mAP@0.5
achieved the perfect barcode recognition capability during the training. However, the
average mAP@(0.5–0.95) is always lesser since the models rely on a higher overlapping
percentage between ground truths and the precited ones. Although the mAP results
from different D-CNN methods are varied, the overall results gradually improve with the
increased degree of augmentation settings (~10 to 20). This evidence confirms that the
augmentation approach dramatically boosts the overall D-CNN performance and decreases
overfitting. When more augmentation degree is considered, the execution results of the
models are slightly better at a higher number of epochs (~50 to 100), as can be observed
in Figures 4–8(d). On different image-augmented distributions, the detection accuracy

Sensors 2022,22, 8788 17 of 27
observed in InventBar and ParcelBar is nearly stabilized. Their mAP variations were very
small when tested on a large number of epochs with intensively augmenting the images,
except only in the case of YOLO x, which shows massive fluctuations.
Sensors 2022, 22, x FOR PEER REVIEW 18 of 28
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 6. Recognition rate of RetinaNet applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 7. Recognition rate of YOLO v5 applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
Figure 6. Recognition rate of RetinaNet applied over seven public barcode datasets.
Sensors 2022, 22, x FOR PEER REVIEW 18 of 28
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 6. Recognition rate of RetinaNet applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 7. Recognition rate of YOLO v5 applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
Figure 7. Recognition rate of YOLO v5 applied over seven public barcode datasets.

Sensors 2022,22, 8788 18 of 27
Sensors 2022, 22, x FOR PEER REVIEW 18 of 28
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 6. Recognition rate of RetinaNet applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 7. Recognition rate of YOLO v5 applied over seven public barcode datasets.
(a) No Augmentation
(b) 5-Degree Augmentation
Sensors 2022, 22, x FOR PEER REVIEW 19 of 28
(c) 10-Degree Augmentation
(d) 20-Degree Augmentation
Figure 8. Recognition rate of YOLO x applied over seven public barcode datasets.
When focusing on the models, RetinaNet and Faster R-CNN are less sensitive to the
weight parameters, i.e., epochs number, augmentation degree, and IoU. Another im-
portant observation is that the results of both RetinaNet and Faster R-CNN are almost
similar in all experimental scenarios. This situation highlights the performance and sta-
bility of some underexplored methods such as RetinaNet when applied to a new appli-
cation domain. Apart from the YOLO x, utilizing all employed methods practically bene-
fits detecting barcodes and is also possible for our two invented datasets.
From the experiments, we were able to perceive that detecting barcodes in the SCM
domain should be done with a high degree of detecting confidence, and YOLO v5 is the
best solution among all employed methods. It is proved that some of the D-CNN meth-
ods that were previously used in different domains, e.g., YOLO v5, EfficientDet, and
RetinaNet, can be precisely applied in a new SCM environment. Apart from the perfor-
mance of D-CNN approaches, the unique and real-world characteristics of recent public
barcode datasets in the field are also key influences challenging the barcode recognition
tasks. However, the originally embedded features of real-world barcode images are
sometimes insufficient for the learning process. Increasing the epoch numbers and aug-
mentations is a way to enhance the model training process and improve the model’s ac-
curacy in detecting barcode images. This is a vital issue that needs to be considered since
better barcode localization results consequently lead superior positive impact for decod-
ing barcode information in the actual SCM industry, e.g., reducing operation mis-
takes/decoding errors, increasing speed, and saving cost. Hence, this investigation rec-
ommends that researchers or practitioners should train and test the D-CNN-based bar-
code recognition methods with sufficient learning iterations and loops of transfor-
mations.
4.3. Runtime Performance
In this section, we evaluate the effect of D-CNN methods on each dataset based on
runtime performance at the optimal accuracy results (mAP@(0.5–0.95)). To verify the
tendency of time required to complete the training process, we also present the perfor-
mance of each model from the dimensions of average runtime.
As illustrated in Table 7, YOLO v5 has shown the greatest runtime performance in a
series of ArteLab Barcode Datasets, while EfficientDet can recognize barcodes and learn
faster than other methods for WWU Muenster, InventBar, and ParcelBar. This evidence
reflects the outstanding performance of these two D-CNN models in providing high de-
tection accuracy but using comparatively low effort. In the dimension of average
runtime shown in Table 8, YOLO x outperforms other D-CNN methods in all datasets.
This result causes no doubt for us because YOLO x is the latest object detection solution
adopted in this study. It is well-known for reducing computational costs and improving
inference speed. One can also see that all D-CNN methods spent much more time train-
ing the WWU Muenster, InventBar, and ParcelBar, most of which required up to an hour
to complete the training task. These large datasets are ranked as the top three with the
highest barcode images. Thus, we assumed that the more extensive the barcode dataset,
Figure 8. Recognition rate of YOLO x applied over seven public barcode datasets.
When focusing on the models, RetinaNet and Faster R-CNN are less sensitive to the
weight parameters, i.e., epochs number, augmentation degree, and IoU. Another important
observation is that the results of both RetinaNet and Faster R-CNN are almost similar in all
experimental scenarios. This situation highlights the performance and stability of some
underexplored methods such as RetinaNet when applied to a new application domain.
Apart from the YOLO x, utilizing all employed methods practically benefits detecting
barcodes and is also possible for our two invented datasets.
From the experiments, we were able to perceive that detecting barcodes in the SCM
domain should be done with a high degree of detecting confidence, and YOLO v5 is the best
solution among all employed methods. It is proved that some of the D-CNN methods that
were previously used in different domains, e.g., YOLO v5, EfficientDet, and RetinaNet, can
be precisely applied in a new SCM environment. Apart from the performance of D-CNN
approaches, the unique and real-world characteristics of recent public barcode datasets in
the field are also key influences challenging the barcode recognition tasks. However, the
originally embedded features of real-world barcode images are sometimes insufficient for
the learning process. Increasing the epoch numbers and augmentations is a way to enhance
the model training process and improve the model’s accuracy in detecting barcode images.
This is a vital issue that needs to be considered since better barcode localization results
consequently lead superior positive impact for decoding barcode information in the actual
SCM industry, e.g., reducing operation mistakes/decoding errors, increasing speed, and
saving cost. Hence, this investigation recommends that researchers or practitioners should
train and test the D-CNN-based barcode recognition methods with sufficient learning
iterations and loops of transformations.
4.3. Runtime Performance
In this section, we evaluate the effect of D-CNN methods on each dataset based
on runtime performance at the optimal accuracy results (mAP@(0.5–0.95)). To verify the
tendency of time required to complete the training process, we also present the performance
of each model from the dimensions of average runtime.
As illustrated in Table 7, YOLO v5 has shown the greatest runtime performance in a
series of ArteLab Barcode Datasets, while EfficientDet can recognize barcodes and learn
faster than other methods for WWU Muenster, InventBar, and ParcelBar. This evidence

Sensors 2022,22, 8788 19 of 27
reflects the outstanding performance of these two D-CNN models in providing high
detection accuracy but using comparatively low effort. In the dimension of average runtime
shown in Table 8, YOLO x outperforms other D-CNN methods in all datasets. This result
causes no doubt for us because YOLO x is the latest object detection solution adopted in
this study. It is well-known for reducing computational costs and improving inference
speed. One can also see that all D-CNN methods spent much more time training the WWU
Muenster, InventBar, and ParcelBar, most of which required up to an hour to complete the
training task. These large datasets are ranked as the top three with the highest barcode
images. Thus, we assumed that the more extensive the barcode dataset, the more time is
required to train the models. One more interesting point is that the size of ParcelBar is
slightly larger than WWU Muenster (both contain a very close number of barcode images).
However, the time consumed for D-CNN methods on ParcelBar is always lesser than the
time spent training the WWU Muenster. Clearly, the dataset size is not only a key influence
on time complexity but also includes the image properties, e.g., a certain amount of barcode
tags, image background, and illumination. These characteristics would have a large effect
on the model’s performance.
Table 7. Runtime performances of D-CNN methods at the optimal detection accuracy.
Datasets EfficientDet Faster
R-CNN RetinaNet YOLO v5 YOLO x
Arte-Lab (Set 1) 0:45:28 1:12:20 1:43:49 0:32:17 1:20:11
Arte-Lab (Set 2) 0:51:24 1:21:05 1:09:27 0:29:30 1:34:02
Arte-Lab Rotated 2:24:35 2:16:44 0:44:22 0:12:20 2:11:01
WWU Muenster 2:57:38 3:23:15 9:36:58 4:22:45 7:34:19
1D Barcode
Extended 0:39:08 0:14:06 1:07:39 1:17:35 0:54:28
InventBar 1:10:40 1:50:38 4:57:20 4:27:19 3:51:31
ParcelBar 1:35:02 2:25:25 3:38:58 1:48:22 5:44:34
Total runtime 110:23:55 12:43:33 22:58:33 13:10:08 23:10:06
1
The runtime performance at the optimal detection accuracy is acquired by mAP@(0.5–0.95) and is presented in
hh:mm:ss.
Table 8.
Average runtime performances of D-CNN methods applied over seven public bar-
code datasets.
Datasets EfficientDet Faster
R-CNN RetinaNet YOLO v5 YOLO x
Arte-Lab (Set 1) 0:24:25 0:35:05 0:26:20 0:26:41 0:20:40
Arte-Lab (Set 2) 0:25:54 0:40:14 0:31:13 0:27:32 0:22:52
Arte-Lab Rotated 0:36:39 0:56:25 0:41:57 0:41:27 0:31:53
WWU Muenster 2:05:51 2:21:52 2:17:29 1:57:52 1:49:48
1D Barcode
Extended 0:15:36 0:22:51 0:17:25 0:19:23 0:13:52
InventBar 1:03:21 1:32:39 1:10:38 0:59:40 0:53:27
ParcelBar 1:30:38 2:19:18 1:43:05 1:39:07 1:19:54
Total runtime 26:22:24 8:48:24 7:08:07 6:31:42 5:32:26
2
The average runtime performance was calculated from all experimental scenarios and represented in hh:mm:ss.
At this stage, we also explore the correlation between the accuracy result defined by
mAP and the runtime performance of different D-CNNs on each dataset. From Figure 9, all
D-CNN methods satisfy high detection accuracy with reasonable runtime. We can clearly
see that one of the YOLO v5 is always positioned at the left-hand side of the scatter chart,
excluding the 1D Barcode Extended dataset and InventBar. Compared to the competitive
methods, the position of YOLO v5 implies a high accuracy with a negligible drop in
runtime. It is noticeable that YOLO v5 consistently outperforms YOLO x in either accuracy
or execution time or both, as shown in Figure 9a–g. Our experimental result is consistent

Sensors 2022,22, 8788 20 of 27
with the study from Gillani et al. 2022 [
89
], who confirmed the higher AP of YOLOv5
than that of YOLO x. We emphasize that using YOLO v5 on ParcelBar, WWU Muenster,
and a series of Arte-Lab datasets will greatly benefit the model training in both accuracy
and time dimensions. For our proposed InventBar, although YOLO v5 has the highest
accuracy, it requires a higher time consumption. Regarding this issue, Faster R-CNN is
highly suggested to apply on the InventBar with the hope of increasing opportunity for
real-time barcode detection in the SCM.
Sensors 2022, 22, x FOR PEER REVIEW 21 of 28
(a) (b)
(c) (d)
(e) (f)
(g)
Figure 9. Runtime performances of the D-CNN methods applied over seven public barcode da-
tasets: (a) Arte-Lab Medium Barcode (Set 1), (b) Arte-Lab Medium Barcode (Set 2), (c) Arte-Lab
Rotated Barcode, (d) WWU Muenster, (e) 1D Barcode Extended, (f) InventBar, and (g) ParcelBar.
4.4. Application Effects of D-CNNs on 1D Barcode Recognition
For the sake of completeness, we continually discussed the application effects of
different D-CNN methods on the 1D barcode recognition, as summarized in Table 9.
Through mainstream single-stage D-CNN network models, EfficientDet and RetinaNet
have never been explored in the barcode detection domain. EfficientDet is a scalable ob-
ject detection method, as it can be applied to a wide range of resource constraints. Its
network architecture can be optimized by jointly scaling up network width, depth, and
Figure 9.
Runtime performances of the D-CNN methods applied over seven public barcode datasets:
(
a
) Arte-Lab Medium Barcode (Set 1), (
b
) Arte-Lab Medium Barcode (Set 2), (
c
) Arte-Lab Rotated
Barcode, (d) WWU Muenster, (e) 1D Barcode Extended, (f) InventBar, and (g) ParcelBar.

Sensors 2022,22, 8788 21 of 27
4.4. Application Effects of D-CNNs on 1D Barcode Recognition
For the sake of completeness, we continually discussed the application effects of
different D-CNN methods on the 1D barcode recognition, as summarized in Table 9.
Through mainstream single-stage D-CNN network models, EfficientDet and RetinaNet
have never been explored in the barcode detection domain. EfficientDet is a scalable
object detection method, as it can be applied to a wide range of resource constraints. Its
network architecture can be optimized by jointly scaling up network width, depth, and
resolution. The model seems better at detecting 1D barcodes in a large dataset, i.e., WWU
Muenster and ParcelBar, with an excellent running speed but comparatively low accuracy.
Under similar accuracy constraints, EfficientDet most often outperforms RetinaNet only
at the cost of inference speed. This is because the RetinaNet considers hard samples
(e.g., extreme foreground-background images) plus two task-specific subnetworks that
yield high detection accuracy as close to the two-stage detectors’ performance but still
taking a long runtime.
Table 9. Application effects of D-CNN methods on 1D barcode recognition.
D-CNN
Methods Model Type Effects on 1D Barcode Recognition
EfficientDet Sigle-stage
•
The model seems better at detecting 1D barcodes in large
datasets, i.e., WWU Muenster and ParcelBar, with a
small running speed.
•At the same detection accuracy level, EfficientDet is
often faster than RetinaNet.
•
The method required less time than other methods at the
best accuracy result. It saved at least two hours during
the inference process on all barcode datasets. Thus, the
method might be practically applied for detecting a large
number of barcode instances in various warehouses
when time is limited.
Faster R-CNN Two-stage
•The model’s overall detection accuracy and running
speed are moderate compared with other
D-CNN solutions.
•Faster R-CNN tends to perform relatively fast on large
datasets containing a number of medium-sized barcodes,
i.e., InventBar, ParcelBar, and WWU Muenster.
RetinaNet Single-stage
•RetinaNet yields high detection accuracy as close to the
performance of Faster R-CNN.
•Considering the optimal accuracy constraint, RetinaNet
consumed lots of time as similar to YOLO x.
•RetinaNet might contribute to complex background
images or real-time barcode detection rather than still
and simple barcode images.
YOLO v5 Single-stage
•YOLO v5 can decrease training time while increasing
barcode detection accuracy.
•The model might be suitable for detecting 1D barcodes,
either a small or large dataset.
•
YOLO v5 is considered robust even if applied on a board
range of barcode sizes or far away barcode objects and
image qualities.

Sensors 2022,22, 8788 22 of 27
Table 9. Cont.
D-CNN
Methods Model Type Effects on 1D Barcode Recognition
YOLO x Single-stage
•YOLO x performs less accurately but much more speed
than other D-CNNs.
•The method needs higher computational efforts, i.e.,
time and iteration numbers, to achieve the best
detection accuracy.
•Still or real-world captured images without or less
augmentation might be one of the more useful settings
for the YOLO x.
One can be observed that the best detection accuracy achieved by EfficientDet, Reti-
naNet, and the two-stage Faster R-CNN sticks together at the same level. This situation
reflects the two-stage detectors, i.e., Faster R-CNN is not always practically benefitting
the barcode detection in the SCM domain, even though many previous studies in barcode
recognition have proven it. Faster R-CNN uses region proposals to localize barcode objects
within the images instead of looking at the complete image, thus providing fairly good
barcode detection accuracy and runtime.
Among all comparable D-CNNs, YOLO v5 shows the most distinguishing characteris-
tics. The method falls within a single convolutional network model to predict the bounding
boxes and the class probabilities for the boxes. It is a hyperparameter evolution method
containing multiple variants, thus having size and inference time tradeoffs. Notably, YOLO
v5 can improve the training convergence time for 1D barcode detection while increasing
model accuracy. The model seems suitable for detecting barcodes from small to large
volumes with a broad range of barcode sizes and image qualities.
In contrast, YOLO x performs less accurately but much more speed (average runtime)
than others in almost all datasets even though it is the latest improved method adopted in
this study and was claimed for a new high performance exceeding previous versions of the
YOLO family [
24
,
90
]. YOLO x uses decoupled head architecture instead of coupled head to
separately perform the classification and localization processes by aiming at higher accuracy
achievement. Nevertheless, the experimental results show that the method needs higher
computational efforts to achieve the best detection accuracy. This might be due to the size
of the YOLO x model being larger than YOLO v5, and the model contains a greater number
of parameters (9 million parameters for YOLO xs and 7.2 million parameters for YOLO-
v5s [
90
]). Another assumption is that the YOLO x model was introduced using strongly
augmented data helping the model to generalize and rely on more features. However,
some data augmentations from YOLO x might not be appropriate for detecting real-world
barcode images. Intuitively, overstepping augmented barcodes and limiting epoch number
at 100 maximum from our experiments might be key reasons affecting the model to decrease
the accuracy. Therefore, using still images and increasing training iterations appear to be
the more useful setting for the YOLO x.
From the detailed analysis above, it is undeniable that the performance of D-CNNs
depends on both network architecture and training settings. All methods can detect
barcodes with high accuracy but largely differ in learning speed. In the SCM environment,
1D barcode detection must be further improved to meet zero detection error, especially
in real-time detection. Therefore, designing a more effective D-CNN model considering
various key influential factors such as image features, dataset characteristics, and barcode
recognition environment would be a great challenge to barcode recognition development.
5. Conclusions
This work proposed the problem of D-CNN-based barcode recognition for supply
chain management. In this context, reliable and fully completed barcode datasets are
required to model and enhance the recognition capability of the D-CNN solutions. This

Sensors 2022,22, 8788 23 of 27
work put forward the two innovative barcode datasets: InventBar and InventBar, by consid-
ering barcode images attached to consumer goods and parcel boxes in the express delivery
warehouse. The proposed barcode data were from real-life images collected from an indoor
warehouse and without simulated data presented in the datasets. Five state-of-the-art and
underexplored D-CNN models were trained and tested over the two proposed datasets, to-
gether with other publicly available barcode datasets. The performance of each model was
analyzed both in terms of mAP and runtime. Benchmarking experiments on all datasets
showed that YOLO v5 performs comparatively better than other methods, especially when
the optimal accuracy rate is on our focus. The mAP@(0.5–0.95) of YOLO v5 achieved an
average of 0.913 from all datasets and reached the maximum of 0.936 in ArteLab (Set 1).
Comparing runtime performance at the best mAP results, EfficientDet spent less time
recognizing barcode instances in large datasets, i.e., in WWU Muenster, InventBar, and
ParcelBar. Instead, YOLO x has shown to be the fastest model when the average runtime
of all experimental scenarios is considered. When investigating the relationship between
runtime and accuracy, YOLO v5 works best on our ParcelBar (
mAP@(0.5–0.95) = 0.918
)
while reasonably satisfying the barcode detection with relatively low training time re-
quirements (nearly one time faster than the other four D-CNN models). Hence, we can
confirm the feasibility of using YOLO v5 with the ParcelBar dataset for detecting barcodes
with sufficient speed and accuracy. For InventBar, however, the Faster R-CNN is highly
suggested, especially when the time dimension is put as a first priority. To this end, our
study also contributes to the notion that some D-CNN methods, mostly adopted in dif-
ferent but unrelated domains, could precisely expand to the realm of possibility in the
SCM application. For future work, real-time barcode localization and decoding in a smart
warehouse or SCM environment should be investigated to make the D-CNNs more effective
for barcode recognition. On this basis, two possible implementation solutions should be
considered. First, implementing either a novel or an improved D-CNN-based barcode
recognition on still images and head-up images (the well-prepared barcode image datasets)
should be enhanced to flawless accuracy at real-time detection. Second, the application of
small barcode or far away barcode detection from remote sensing technologies and aerial
objects, i.e., drones in the indoor warehouse, should also be explored in the next research.
Under the conditions of satisfying the performance requirements of the D-CNNs on 1D
barcode recognition, future research could be designed by taking into account the scanning
technologies, aerial image features, type of barcodes, and warehouse environment.
Author Contributions:
Conceptualization, T.K., P.C., C.M. and R.W.; methodology, T.K., P.C., C.M.
and R.W.; software, T.K. and P.C.; validation, T.K. and P.C.; formal analysis, T.K., C.M. and R.W.;
investigation, T.K., P.C., C.M. and R.W.; resources, R.W. and C.M.; data curation, T.K., P.C., C.M. and
R.W.; writing—original draft preparation, T.K., C.M. and R.W.; writing—review and editing, T.K.,
C.M. and R.W.; visualization, C.M.; supervision, R.W.; project administration, C.M.; and funding
acquisition, R.W. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the National Research Council of Thailand (NRCT), Chiang
Mai University (CMU), and College of Arts, Media, and Technology (CAMT) under the Mid-career
Researcher Grant (Grant number: NRCT5-RSA63004-05).
Institutional Review Board Statement:
This research was conducted in accordance with the Declara-
tion of Helsinki, the International Conference in Harmonization in Good Clinical Practice (ICH-GCP),
and the Belmont Report, and the research protocol was approved by the Chiang Mai University
Research Ethics Committee (CMUREC No. 62/147 and COE No. 036/62).
Data Availability Statement:
Publicly available datasets were analyzed in this study. This data can
be found here: https://cmu.to/BenchmarkBarcodeDatasets (accessed on 13 October 2022).
Acknowledgments:
This work was supported the National Research Council of Thailand, Chiang
Mai University, and the College of Arts, Media, and Technology. We would like to thank the
anonymous reviewers who made valuable suggestions to improve the quality of the research.
Conflicts of Interest: The authors declare no conflict of interest.

Sensors 2022,22, 8788 24 of 27
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