Workshop safety helmet detection algorithm based on improved YOLOv5

Yushuang Wang; Yi Qiao; Wen Li

doi:10.1117/12.2675170

25 May 2023 Workshop safety helmet detection algorithm based on improved YOLOv5

Yushuang Wang, Yi Qiao, Wen Li

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263624 (2023) https://doi.org/10.1117/12.2675170
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

For workshop workers, helmets are one of the most important protective tools. Wearing helmets during operation can further protect the lives of workers. Aiming at the situation that the current safety helmet detection model has missed detection and false detection of small targets and dense targets in complex environments such as workshops, this paper proposes a workshop operation safety helmet detection algorithm based on improved YOLOv5 in combination with practical application scenarios. The CBAM attention mechanism is added to the backbone network, so that the network pays more attention to the safety helmet target to be detected, which can improve the detection effect in complex backgrounds. The EIoU _ Loss function is used to replace the GIoU _ Loss function to improve the convergence effect of the module. By comparing the accuracy and detection speed of the improved YOLOv5 target detection algorithm with the original YOLOv5 target detection algorithm on the self-made safety helmet detection data set, the results show that the average accuracy of the improved YOLOv5 model is 1.4 % higher than that of the original model. Achieved in a complex environment for small targets and dense target detection requirements.

Citation Download Citation

Yushuang Wang, Yi Qiao, and Wen Li "Workshop safety helmet detection algorithm based on improved YOLOv5", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263624 (25 May 2023); https://doi.org/10.1117/12.2675170

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