Research on helmet wear detection algorithm based on improved YOLOv5s

Wanzhen Zhou; Shuo Li; Huicong Wu

doi:10.1117/12.3040519

10 September 2024 Research on helmet wear detection algorithm based on improved YOLOv5s

Wanzhen Zhou, Shuo Li, Huicong Wu

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325712 (2024) https://doi.org/10.1117/12.3040519
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Which aims to address the shortcomings of current mainstream target detection methods in detecting small helmet-wearing targets, reducing the miss rate and improving detection accuracy. This work introduces a new target detection technique based on improved YOLOv5s.First, to increase the detection accuracy of the small target helmet, a 160×160 small target detection layer is added to the original model. Secondly, AFPN network structure is added to the neck part of YOLOv5s to carry out asymptotic multi-scale feature fusion to reduce information loss or degradation in multi-level transmission. Finally, in order to improve the EIoU loss function, inner ideas are presented., original function replaced by inner-EIoU, so as to improve the learning ability of samples under complex background. Experimental findings on the helmet dataset SHWD show that the improved algorithm model map@0.5 and map@0.5:0.95 reach 95.4% and 62%, 1.7% and 1% over the YOLOv5s model, respectively, and has better detection effect.

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

Citation Download Citation

Wanzhen Zhou, Shuo Li, and Huicong Wu "Research on helmet wear detection algorithm based on improved YOLOv5s", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325712 (10 September 2024); https://doi.org/10.1117/12.3040519

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