Helmet wearing detection algorithm based on improved YOLOv5

Ying Hu; HaiKun Chang; RanSheng Yang

doi:10.1117/12.2684654

21 July 2023 Helmet wearing detection algorithm based on improved YOLOv5

Ying Hu, HaiKun Chang, RanSheng Yang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 1271727 (2023) https://doi.org/10.1117/12.2684654
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

In order to solve the problem that the existing helmet detection algorithms have low accuracy in detecting dense small targets in a complex environment, helmet wearing detection algorithm based on improved YOLOv5 is proposed. Firstly, the original CSP structure is replaced by a new redesigned DB_CSP module to improve the ability of backbone network feature extraction. Secondly, the coordinate attention mechanism is added in the backbone feature network layer to make the model pay more attention to small targets. Then, Bidirectional Feature Pyramid Network (BiFPN) is used to replace PANet to enhance the ability of feature fusion. Finally, a special detection layer for small targets is added to form four scale detection. The experimental results show that the mAP of this model is 92.28%, which is 4.9% higher than that of YOLOv5s model. The average detection time is 0.0276 seconds, which improves the detection accuracy while maintaining the model speed.

Citation Download Citation

Ying Hu, HaiKun Chang, and RanSheng Yang "Helmet wearing detection algorithm based on improved YOLOv5", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 1271727 (21 July 2023); https://doi.org/10.1117/12.2684654

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KEYWORDS

Target detection

Feature fusion

Small targets

Convolution

Feature extraction

Neck

Mathematical optimization

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