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Xie Liu

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Proceedings Article | 27 March 2024 Paper

Identification of illegal behaviors of sand trucks based on improved YOLO V5S

Xiangyu He, Jie Zhong, Mingfu Zhao, Le Chen, Xie Liu, Yuanguo Su

Proceedings Volume 13105, 131053E (2024) https://doi.org/10.1117/12.3026385

KEYWORDS: Performance modeling, Object detection, Detection and tracking algorithms, Target detection, Data modeling, Visual process modeling

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With the needs of urban infrastructure work, the number of sand trucks is increasing. Monitoring the illegal behavior of sand trucks is a highly repetitive and wasteful position of manpower and material resources. This paper presents an improved model based on YOLO V5s to identify the illegal behavior of sand trucks. This paper proposes the CA attention mechanism module to improve the network's attention to channel information, and then uses the ACON-C activation function to introduce two learnable dynamic parameters to the activation function to increase the nonlinearity of the network. Finally, the MPDIoU loss function is used. After the structure is improved, the same training parameters and data sets are used for different models through comparative experiments. The results show that the improved algorithm improves the mAP@0.5 index by 6.8%, the accuracy rate P and the recall rate R by 5.3% and 8.1%, respectively, compared with the original algorithm.

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