Research on pedestrian detection algorithm based on improved YOLOv5

Chang Han; Quanyu Wang; Yanling Li

doi:10.1117/12.3035333

19 July 2024 Research on pedestrian detection algorithm based on improved YOLOv5

Chang Han, Quanyu Wang, Yanling Li

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321326 (2024) https://doi.org/10.1117/12.3035333
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

YOLOv5 has been improved to solve the problems such as low speed and accuracy of pedestrian detection task and insufficient feature fusion in complex scenarios. First, the GhostBottleneck module replaces the existing BottleneckCSP submodule for algorithm backbone networks, which improves the detection speed. Then, CBMA module is introduced to make the model enhance the ability of accurate positioning of small targets to improve target detection capability. Finally, SIoU_loss is used as the boundary box regression loss function to effectively predict the distance between the boundary box and the real boundary box, and improve the detection accuracy of the algorithm. The experimental results indicate that the improved algorithm can effectively improve the accuracy of pedestrian target detection.

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

Citation Download Citation

Chang Han, Quanyu Wang, and Yanling Li "Research on pedestrian detection algorithm based on improved YOLOv5", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321326 (19 July 2024); https://doi.org/10.1117/12.3035333

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