Helmet wearing detection algorithm based on improved YOLOv8

Jinhu Hu; Yanfei Chen; Tiange Huang; Gang Wang

doi:10.1117/12.2692365

7 March 2024 Helmet wearing detection algorithm based on improved YOLOv8

Jinhu Hu, Yanfei Chen, Tiange Huang, Gang Wang

Proceedings Volume 13085, MIPPR 2023: Automatic Target Recognition and Navigation; 1308507 (2024) https://doi.org/10.1117/12.2692365
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Aiming at the problems of low detection accuracy and slow detection speed of existing helmet detection models in complex environments, an improved YOLOv8n helmet wearing detection algorithm is proposed in this paper. Firstly, CBAM attention mechanism is added to the backbone network to strengthen the feature extraction capability of the backbone network. Then SimSPPF module is used to replace SPPF module in backbone network to improve the speed of model detection. Finally, DIoU-NMS is used instead of NMS to enhance the detection of occluded targets. The experimental results show that the average detection accuracy of the improved YOLOv8n algorithm is 94.85% and the detection speed is 109.11 FPS, which is 1.41% higher than that of the improved YOLOv8n algorithm.

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

Citation Download Citation

Jinhu Hu, Yanfei Chen, Tiange Huang, and Gang Wang "Helmet wearing detection algorithm based on improved YOLOv8", Proc. SPIE 13085, MIPPR 2023: Automatic Target Recognition and Navigation, 1308507 (7 March 2024); https://doi.org/10.1117/12.2692365

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