Considering the problems of difficult target detection and recognition and low accuracy caused by factors such as uneven illumination, poor working conditions, complex structure of tank guide and narrow space in coal ...
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Considering the problems of difficult target detection and recognition and low accuracy caused by factors such as uneven illumination, poor working conditions, complex structure of tank guide and narrow space in coal mine. This paper simulates the complex working environment of the underground mine to carry out different fault conditions experiments, and establishes four categories of channel fault picture data sets. In order to improve the detection accuracy and speed, the following improvements are made on the basis of the yolov7algorithm, and our algorithm is constructed: (1) attention mechanisms are added at different locations of the network;(2) replacement loss function;(3) the original coupling detection head of yolov7 is replaced by an efficient decoupled head with implicit knowledge learning. The experimental results show that the mean average precision (mAP) of our algorithm model proposed in this paper reaches 93.2% when the Intersection over Union (IoU) threshold is 0.5, which is 3.2% higher than that of yolov7 itself, and the detection speed is also relatively improved by 15.76 frames per second (FPS), reaching 107.50 FPS. While solving the problem of unbalanced improvement of detection accuracy and speed, it also effectively reduces the number of parameters and calculation of the network, which verifies the feasibility of the improvedalgorithm in this paper.
Advancements in real-time processing for object detection have significantly improved the accuracy of detection algorithms. These improvements are important for precision spraying technologies, enabling more focused a...
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Advancements in real-time processing for object detection have significantly improved the accuracy of detection algorithms. These improvements are important for precision spraying technologies, enabling more focused and efficient approaches that are fundamental to the progress of precision agriculture. In this study, a real-time grape vineyard leaf detection system is presented to specifically identify and spray unhealthy leaves, improving pesticide application efficiency. The system employs an improvedyolov7 deep learning algorithm, capable of classifying grapevine leaves into three categories: unhealthy leaves, healthy leaves, and grape cluster bags. A lightweight convolution layer was integrated into the algorithm's backbone for better generalization and feature extraction, making the model more adaptable across various data types. Then, a squeeze and excitation block coupled with the batch normalization block was incorporated to assess each channel's significance. This addition merges spatial and channel-wise information within each layer's local receptive field. An adaptive gradient optimizer coupled with Lasso regularization was implemented for improved generalization and better handling of noisy data. An ELU activation function was added to better converge and regularize the model, and a GELU activation function was exchanged to introduce non-linearity and reduce vanishing gradient points. A total of 2300 images of grape leaves were taken from the vineyard and labeled with LabelImg annotation tool. The results of the improved yolov7 algorithm showed a 3.2 % improvement in Precision, 6.2 % in Recall, 1.6 % in mAP@0.5, and 7.1 % in mAP@0.5:0.95. To verify the effectiveness of the proposed method, the results were compared with Faster RCNN, RetinaNet R50-FPN, Double Head RCNN, yolov5, yolov7, and yolov9. To evaluate the pesticide coverage outcome, the improved results file was then taken for an outdoor experiment which showed 65.96 % improvement in spraying pes
The normal operation of a integrated hub station is of great significance for the safe operation of the entire city's transportation network. Accurately monitoring the passenger flow operation status of the statio...
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The normal operation of a integrated hub station is of great significance for the safe operation of the entire city's transportation network. Accurately monitoring the passenger flow operation status of the station is the fundamental basis for achieving scientific management and control of passenger flow. In response to the urgent need for accurate and real-time detection of passenger flow in station passageways, a yolov7-based improved Deep-Sort algorithm is proposed to detect and track bi-directional passenger flow in the passageways of integrated hub stations. Based on the yolov7 detection algorithm, the SimAM attention mechanism was introduced to improve the accuracy of detecting passenger flow in the passageways. On the basis of the Deep-Sort tracking algorithm, the Kalman Filter (KF) method was optimized to make the tracking box of the target more accurate. Meanwhile, the Fast-ReID method was used to improve the long-term tracking of targets, thereby improving the value of IDF1. This algorithm can help to achieve real-time and accurate detection and tracking of bi-directional passenger flow in station passageways. In the event of an abnormal situation, the station staff can react rapidly to improve the station's operational safety.
Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deep learning-based crack image focuses more on cracks at a certain moment and ignores the full-time cra...
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Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deep learning-based crack image focuses more on cracks at a certain moment and ignores the full-time crack expansion details, which are crucial for more reasonable evaluation and safety quantification of concrete structures. This paper proposes a new method based on the combination of improved You Only Look Once v7 (yolov7) algorithm, crack expansion benchmark method, improved DeepLabv3+ algorithm, and image pro-cessing technology to monitor the whole process of crack development, including real-time crack recognition and real-time monitoring of crack dynamic expansion. The precision of the improved detection algorithm can be improved by a maximum of 5.34%, and the mean intersection over union (mIoU) of the improved segmentation algorithm can be improved by 0.15%, resulting in better segmentation results. The experimental results show that this method can efficiently and accurately achieve real-time tracking of crack dynamic expansion, especially for monitoring of tiny cracks.
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