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Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV...
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... While the system achieved over 80% counting accuracy, it was not evaluated on the real number of vessels passing through the monitored zone. In [6], an NN based object detector is used for vessel target tracking and self-correction counting in waterway scenes using YOLOv3. To overcome the challenges of target drift and jitter, the authors propose a self-correcting network that combines regression-based direction determination and a target counting method with variable time windows. ...
... However, it had problems tracking fast-moving vessels and was not assessed against the actual number of vessels passing through the monitored zone. The authors in [6] used an NN-based object detector and classifier for vessel counting in waterway scenes, but it was trained for a single general class for all vessels. In [7], on the other hand, the authors trained their system for five large classes of vessels, which do not match the maritime traffic profile of passenger ports. ...
Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.
... Two hundred different meter images were selected for input into the system to test the recognition effect of the model, and some of the recognized images are shown in Figure 4. e loss during training was recorded and plotted as a loss curve and compared with the model used in [1]. As can be seen from Figure 5, the YOLOv3 model [23][24][25] used in this study has a lower loss value and is better able to achieve the recognition of industrial meter types. ...
Aiming at the demand of industrial instrument reading, this study proposes a method of industrial instrument classification and reading recognition based on YOLOv3. Given that industrial meters can be divided into pointer meters and digital meters according to the dial type, this method conducts a reading study for each of the two types of meters. Firstly, the YOLOv3 model is trained to recognize and detect the meter types and classify the meters according to the values of the obtained classes. The pointer meter uses a Hough circle to detect the dial, extracts the scale and the pointer, calculates the angle between the 0 scale line and the pointer, and obtains the reading of the pointer meter. The digital meter extracts the digits by finding the contours of the dial and the digit area and then uses a support vector machine (SVM) to identify the extracted digits and output the readings of the digital meter. Through the test, the mean average precision (mAP) of the recognition model in this study is 93.73%. The absolute error of pointer meter reading is less than 0.1 in general, and the maximum relative error is 0.35%. The accuracy of the digital meter reading is 99.7%. The proposed method can accurately read the value of the instrument and meet the needs of industrial production.
... Based on the bounding box predicted in the detect stage, Deep SORT [28] tracking algorithm is considered to track the ship targets in the UAV images. Deep SORT is originated from SORT, which uses Kalman filter and Hungarian Journal of Advanced Transportation algorithm to handle motion prediction and data association problems [29]. Deep SORT consists of four parts: Deep Appearance Descriptor, Kalman filter, Matching Cascade, and Matching Intersection-over-Union, as shown in Figure 3. ...
In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation.
... YOLOv3 performs frame prediction in a clustering manner and uses the predicted 4 values as the parameters to determine the frame prediction. They are the horizontal and vertical axes of the center point of the frame and the width and height of the frame [17]. The four parameters work together to predict the frame, and the determination of the frame target is expressed by the confidence, and the value range of the confidence is ½0, 1. ...
The detection of moving objects by machine vision is a hot research direction in recent years. It is widely used in military, medical, transportation, and agriculture. With the rapid development of UAV technology, as well as the high mobility of UAVs and the wide range of high-altitude vision, the target detection technology based on UAV vision is applied to traffic management such as vehicle tracking and detection of vehicle violations. The moving target detection technology in this study is based on the YOLOv3 algorithm. It implements moving vehicle tracking by means of Mean-Shift and Kalman filtering. In this paper, the Gaussian background difference technology is used to analyze the illegal behavior of the vehicle, and the color feature extraction technology is used to identify and locate the license plate, and the information of the illegal vehicle is entered into the database. The experiment compares the moving target detection of UAV vision and the traditional target detection in four aspects: recognition accuracy, recognition speed, manual time, and divergent results. The results show that the average accuracy rates of UAV vision-based moving target detection and traditional pattern recognition are 98.4% and 87.8%, respectively. The recognition speeds are 24.9 (vehicles/sec) and 10.6 (vehicles/sec), respectively. However, the artificial time and divergence results of moving target detection based on UAV vision are only 1/3 of the traditional mode. The moving target detection based on UAV vision has a better moving target detection ability.
... Chun Liu et al. [21] proposed a model by adjusting and optimizing various parameters. Deep learning is used in conjunction with target HSV color histogram features and target's LBP local features. ...
Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.
... According to the statistics of 2021 Express, the cargo throughput of China's ports above designated size has reached 2.3 billion tons, with a year-on-year growth of 6.4%, the container throughput has increased by 2.27% to 8.3%, and the growth rate is significantly faster than that of the cargo throughout of 6.4%. Among them, the import volume of foreign trade coal has increased significantly, and the growth rate exceeds the expected value [1]. ...
The reading of the ship draft is an important step in the process of weighing and pricing. The traditional detection method is time-consuming and labor-consuming, and it is easy to lead to misdetection. In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. The experimental results show that the ship draft reading method proposed in this paper has obvious effects. The method has a good detection effect on high-quality images, and the accuracy rate can reach 98%. The accuracy rate can also reach 73% for the images with poor quality caused by improper capture, character corrosion, bad weather, etc. This method is a kind of artificial intelligence method with safe measurement process, high measurement effect, and accuracy, providing a new idea for related research.
Automatic and accurate broiler counting plays a key role in the intelligent management of the cage-free broiler breeding industry. However, severe occlusion, similar appearance, variational posture and extremely crowded situation make it a very challenging task to accurately count cage-free broilers by applying the computer vision method. Currently, many broiler breeding enterprises have to count broilers manually, resulting in high management costs. To address these challenges, we propose a novel framework called YOLOX-Birth Growth Death (Y-BGD) for automatic and accurate cage-free broiler counting. The proposed method cooperated with improved multiple-object tracking algorithm to ease tracking loss and counting error by adopting BGD data association strategy. First, to evaluate the proposed framework, we constructed a large-scale dataset (namely ChickenRun-2022) that contains 283 videos, 343,657 label boxes, and over 144,000 frames with 14,373 chicken instances in total. Next, we conducted extensive experiments and analyses on this dataset and compared it with existing representative tracking algorithms to demonstrate the effectiveness of the proposed framework. Finally, the proposed framework yielded 98.131% counting accuracy, 0.1291 GEH, and 58.98 FPS speed on ChickenRun-2022. In conclusion, the proposed method provides an automatic approach to counting the number of cage-free broiler chickens in videos with higher speed and greater accuracy, which will benefit the broiler breeding industry and precision chicken management.
The maritime sector is exploring the applicability of alternative powering options and ways to implement new technologies to increase safety, efficiency, and autonomy of ship power systems. The technological development of power systems, their complexity, and high costs of their malfunction or downtime have led to employment of different approaches in safety engineering. In order to reduce hazards and failures in ship operation, shipbuilders use several methods during design phase to identify, investigate and manage all safety concerns. For this purpose, there is range of methods, as for instance Fault Tree Analysis (FTA), Event Tree Analysis (ETA), Failure Mode Effects Analysis (FMEA), and Failure Mode, Effects and Criticality Analysis (FMECA), which can be used separately or combined. This paper reviews these methods with their advantages and limitations in their application to risk assessment of ship power systems.
