Research on pedestrian detection method for commercial vehicles of blind zone based on improved YOLOv5s

Shujian Wang; Zhiwei Guan; Ruzhen Dou; Guoqiang Wen; Lei Qi; Yanjie Yang

doi:10.1117/12.3033080

5 July 2024 Research on pedestrian detection method for commercial vehicles of blind zone based on improved YOLOv5s

Shujian Wang, Zhiwei Guan, Ruzhen Dou, Guoqiang Wen, Lei Qi, Yanjie Yang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318464 (2024) https://doi.org/10.1117/12.3033080
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Due to the frequent occurrence of pedestrian accidents caused by the blind spot of commercial vehicle drivers, the YOLOv5s-FE algorithm is proposed to detect pedestrians in the blind spot using YOLOv5s as a baseline model for this problem. The Swin Transformer Block structure is introduced in the Neck network to improve the feature extraction capability to capture the global information. To solve the problem of missed detection of small-target pedestrians, the feature fusion part in the network structure is added with an upsampling layer to get a larger size feature map and then fused with shallow features to preserve the localization information of small-target pedestrians. Through experimental verification, the precision, recall and accuracy are improved by 2.2%, 2.1%, and 3.4%, respectively, and the pedestrian target can be detected effectively.

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

Citation Download Citation

Shujian Wang, Zhiwei Guan, Ruzhen Dou, Guoqiang Wen, Lei Qi, and Yanjie Yang "Research on pedestrian detection method for commercial vehicles of blind zone based on improved YOLOv5s", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318464 (5 July 2024); https://doi.org/10.1117/12.3033080

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KEYWORDS

Target detection

Feature extraction

Detection and tracking algorithms

Education and training

Image enhancement

Image processing

Transformers

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