Lightweight helmet wear detection algorithm based on YOLOv3

Tong Liu; Rui Li; Juan Liu; Jiahui Fang

doi:10.1117/12.2658840

23 November 2022 Lightweight helmet wear detection algorithm based on YOLOv3

Tong Liu, Rui Li, Juan Liu, Jiahui Fang

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 1245426 (2022) https://doi.org/10.1117/12.2658840
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

To reduce the casualties caused by workers not wearing safety measures in time at construction sites. For the generic target detection model with high complexity and large model, which cannot perform helmet wearing detection in real-time, a real-time helmet wearing detection algorithm based on YOLOv3 is proposed, with the mobile end network as the feature extractor of the proposed method. For helmet wearing detection is often done outdoors, so to filter the environmental noise. The channel attention module is introduced to optimize the feature extraction when multiplexing the multi-scale feature maps. Finally, to weaken the problem of inadequate gradient back propagation brought by the IOU function, the CIOU loss function is used to optimize the gradient back propagation. The experimental results show that the method in this paper can balance the accuracy and detection rate of the detection model with an average accuracy of 82.47% (mAP). Compared with the model of the original YOLOv3 network, the model size of this method is only 22.3% of the original model, and the detection rate is significantly improved compared with the original method and can meet the requirements of real-time helmet wear detection.

Citation Download Citation

Tong Liu, Rui Li, Juan Liu, and Jiahui Fang "Lightweight helmet wear detection algorithm based on YOLOv3", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 1245426 (23 November 2022); https://doi.org/10.1117/12.2658840

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KEYWORDS

Target detection

Feature extraction

Data modeling

Convolution

Mobile devices

Safety

Sensors

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