A two-step algorithm for detecting helmet wear in electric vehicles based on YOLOv5-FD

Juqian Yang; Ping Hu; Jiashu Dai

doi:10.1117/12.3037806

12 September 2024 A two-step algorithm for detecting helmet wear in electric vehicles based on YOLOv5-FD

Juqian Yang, Ping Hu, Jiashu Dai

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325623 (2024) https://doi.org/10.1117/12.3037806
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

To address the issue of detecting electric vehicle helmet wearing in complex environments where pedestrians and electric vehicles are moving in the same direction, we propose an improved algorithm based on YOLOv5s. To guarantee a lightweight model that enhances accuracy, we replaced the backbone structure of YOLO with FasterNet-T1 and introduced the DSConv module in the neck network of YOLO to reduce model computation without sacrificing accuracy. The experimental results demonstrate that the improved algorithm enhances the mean average precision (mAP) by 3.1% and reduces computation by 7.3% compared to the YOLOv5s algorithm. This improvement ensures higher detection accuracy while reducing computation, making it valuable for certain applications. Additionally, the improved model enhances the generalisation of detection compared to other mainstream detection models, making it applicable to a wider range of detection scenarios.

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

Citation Download Citation

Juqian Yang, Ping Hu, and Jiashu Dai "A two-step algorithm for detecting helmet wear in electric vehicles based on YOLOv5-FD", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325623 (12 September 2024); https://doi.org/10.1117/12.3037806

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KEYWORDS

Detection and tracking algorithms

Target detection

Convolution

Object detection

Feature extraction

Education and training

Image processing

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