Helmet wearing detection method based on lightweight YOLOv5

Chao Wu; De-yong Wang; Wen-xi Shi; Jian Fang; Xueyi Zhao; Yan-yun Fu

doi:10.1117/12.2685779

15 August 2023 Helmet wearing detection method based on lightweight YOLOv5

Chao Wu, De-yong Wang, Wen-xi Shi, Jian Fang, Xueyi Zhao, Yan-yun Fu

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127193S (2023) https://doi.org/10.1117/12.2685779
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

A lightweight detection algorithm based on YOLOv5 is proposed to solve the problems that the target detection algorithm has many network parameters and the large size of the model is not conducive to the actual deployment. Firstly, the depth separable convolution was introduced to reconstruct the backbone network to reduce the network parameters. Then, the residual element was redesigned by ghost convolution and integrated into the Neck end to further reduce the model volume. Finally, the detection accuracy and recognition effect of the algorithm were improved by modifying the detection layer. When the recognition accuracy of the improved lightweight algorithm is reduced by 3.4%, the model volume is reduced by 75%, and the number of network parameters is reduced by 81%. The results show that the algorithm is able to maintain a high recognition accuracy, and the volume and parameters are greatly reduced. It is suitable for embedded and other system resource limited scenarios, and has a practical application prospect.

Citation Download Citation

Chao Wu, De-yong Wang, Wen-xi Shi, Jian Fang, Xueyi Zhao, and Yan-yun Fu "Helmet wearing detection method based on lightweight YOLOv5", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127193S (15 August 2023); https://doi.org/10.1117/12.2685779

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available