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Configuration of the training inputs. Batch. Subdivisions Width, Height Channels Decay Angle Saturation Exposure Hue Learning Rate Burn in Max Batches Policy Steps Scales
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Construction safety accidents occur due to a combination of factors. Even a minor accident that could have been treated as a simple injury can lead to a serious accident or death, depending on when and where it occurred. Currently, methods for tracking worker behavior to manage such construction safety accidents are being studied. However, applying...
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... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Context 2
... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Context 3
... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Context 4
... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Context 5
... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Context 6
... the learning model development, 50% of the acquired images were used for learning, 30% for comparison, and 20% for verification data. The training settings applied to use the system are shown in Table 2. ...Similar publications
In this paper, we propose a class of median-based matrix information geometry (MIG) detectors with a manifold filter and apply them to signal detection in nonhomogeneous environments. As customary, the sample data is assumed to be modeled as Hermitian positive-definite (HPD) matrices, and the geometric median of a set of HPD matrices is interpreted...
Citations
... Ref. Description Anti-counterfeiting [10] Implement and improve the quality of an anti-counterfeiting system through the use of QR codes Augmented reality [11] QR codes for the personalization of objects displayed by virtual reality Automatic config. [12] QR codes used to auto-configure Industrial Internet of Things sensor networks Fast reading [13] Mobile application that allows reading densely placed QR codes Identification [14] QR codes and robotic arms to automatically manage books in a library Localization [15] QR codes used to improve the localization of mobile robots [16] Use of QR codes placed in specific points to track the position of a person [17] Use and placement of QR codes to enable navigation in indoor environments Maintainance [18] QR codes are used to perform predictive maintenance and self-calibration of a robotic arm Healthcare [19] Embedding of an ECG signal within a QR code [20] Use of QR codes for authenticating medical images in a blockchain framework context Payment [21] Improving security when QR codes are used for payment [22] How the use of QR codes for mobile payments is perceived by users [23] Use of QR codes to manage E-Wallets Recycling [24] Use of QR codes to manage radioactive waste [25] Combined use of QR codes and blockchains to build a recycling platform Safety [26] Management of construction safety through QR codes Security [27] Embed a secret within the QR code [28] Manage encryption and decryption through watermarking in medical applications [29] A comprehensive study about QR code applications from the point of view of security and privacy Teaching [30] Integration of QR codes in teaching material and in classrooms to improve the quality of education [31] Integrate QR codes and text to improve the learning of English as a foreign language Traceability [32] Use of blockchains, explainable artificial intelligence, and QR codes for food traceability [33] A review on the possibility of embedding information on manufactured parts [34] Use of traceability to track vegetable supply chain Tourism [35] QR codes for exploring mount Etna (volcano) (e.g., printed on posters or stickers, possibly with additional information) is translated to a binary representation (arrow 4 ). The application executing on the end-user device (e.g., a smartphone) has to recognize and extract the eQR/QR code contained in a digital image (e.g., acquired using the camera of the smartphone), perform the related error correction algorithms, and obtain the sequence of bytes representing the eQRbytecode. ...
QR codes are increasingly used in a plurality of scenarios, and research activities are being successfully carried out to improve this technology and widen its contexts of applicability. After an extensive survey of the state-of-the-art on the subject, this work presents the new, promising possibility to embed a programming language in a QR code. This new kind of executable QR codes, we named eQR codes, enable interaction with end users even in the absence of an Internet connection, and provide a sort of IoT paradigm where intelligence is embedded in the object tag in the form of a program. Among all the possible languages that can be embedded, this work focuses on a powerful but compact (in terms of QR code storage occupation) dialect, termed QRtree, which is aimed at implementing decision trees. The eQR code technology makes a new class of applications possible, e.g., providing hints for navigation or instructions for using rescue devices in places with no network coverage like mountains and caves. Smart interactive user manuals are enabled as well. Besides defining the QRtree language and eQR code structure, this paper describes all the steps needed to generate eQR codes and to manage their execution in end-user devices. A simple yet realistic example and the related code are also presented, to practically show how this technology can be used to solve real-world problems. For the example, the QRtree version of the code takes 234B, less than one-half the size of an equivalent program in Python bytecode (634B).
... They are of special priority for computer systems operating with real (physical) objects that are not directly connected. In applications such as augmented reality (AR) [1][2][3] or surveillance [4,5], identification of relatively small objects in broad scenes is an important factor for their efficiency. The rapid development of novel CV methods, such as deep learning, has opened brand new capabilities to the CV area. ...
For the interacting with real world, augmented reality devices need lightweight yet reliable methods for recognition and identification of physical objects. In that regard, promising possibilities are offered by supporting computer vision with 2D barcode tags. These tags, as high contrast and visually well-defined objects, can be used for finding fiducial points in the space or to identify physical items. Currently, QR code readers have certain demands towards the size and visibility of the codes. However, the increase of resolution of built-in cameras makes it possible to identify smaller QR codes in the scene. On the other hand, growing resolutions cause the increase to the computational effort of tag location. Therefore, resolution reduction in decoders is a common trade-off between processing time and recognition capabilities. In this article, we propose the simulation method of QR codes scanning near limits that stem from Shannon’s theorem. We analyze the efficiency of three publicly available decoders versus different size-to-sampling ratios (scales) and MTF characteristics of the image capture subsystem. The MTF we used is based on the characteristics of real devices, and it was modeled using Gaussian low-pass filtering. We tested two tasks—decoding and locating-and-decoding. The findings of the work are several-fold. Among others, we identified that, for practical decoding, the QR-code module should be no smaller than 3–3.5 pixels, regardless of MTF characteristics. We confirmed the superiority of Zbar in practical tasks and the worst recognition capabilities of OpenCV. On the other hand, we identified that, for borderline cases, or even below Nyquist limit where the other decoders fail, OpenCV is still capable of decoding some information.
... Two-dimensional barcode symbols have been widely used in automated identification applications, such as industrial components retrospect [1], mobile robots [2], and Construction Safety Management [3]. Compared to the 1D barcode, 2D barcode symbols have more popular advantages, such as large data capacity, compact size, and strong fault tolerance [4]. ...
A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.
