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

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

doi:10.1117/12.3026385

27 March 2024 Identification of illegal behaviors of sand trucks based on improved YOLO V5S

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

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131053E (2024) https://doi.org/10.1117/12.3026385
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangyu He, Jie Zhong, Mingfu Zhao, Le Chen, Xie Liu, and Yuanguo Su "Identification of illegal behaviors of sand trucks based on improved YOLO V5S", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131053E (27 March 2024); https://doi.org/10.1117/12.3026385

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KEYWORDS

Detection and tracking algorithms

Object detection

Performance modeling

Data modeling

Target detection

Visual process modeling

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