Vehicle pedestrian detection method based on improved YOLOv5 algorithm

Zhao-hui Chen; Xiao-ming Ling

doi:10.1117/12.3004900

20 October 2023 Vehicle pedestrian detection method based on improved YOLOv5 algorithm

Zhao-hui Chen, Xiao-ming Ling

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129161A (2023) https://doi.org/10.1117/12.3004900
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

Vehicle pedestrian detection is a key aspect in driver assistance systems, which need to accurately detect all vehicle pedestrian targets on the roadway in order to ensure driving safety. To solve the problem of low accuracy in vehicle pedestrian target detection, this paper proposes a vehicle pedestrian detection method based on the improved YOLOv5 algorithm. In this paper, the initial anchor boxes of the dataset are re-clustered by the K-means clustering algorithm, and the CIOU loss function and DIOU_nms, are applied to the YOLOv5 algorithm to improve the target recognition effect and reduce the false and missed detection rate of small targets. The experimental results show that the mAP@0.5 of the improved YOLOv5 algorithm is improved by 1.85%.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhao-hui Chen and Xiao-ming Ling "Vehicle pedestrian detection method based on improved YOLOv5 algorithm", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129161A (20 October 2023); https://doi.org/10.1117/12.3004900

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