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Real-time detection model of electrical work safety belt based on lightweight improved YOLOv5

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Abstract

Aiming at the issue that the existing aerial work safety belt wearing detection model cannot meet the real-time operation on edge devices, this paper proposes a lightweight aerial work safety belt detection model with higher accuracy. First, the model is made lightweight by introducing Ghost convolution and model pruning. Second, for complex scenarios involving occlusion, color confusion, etc., the model’s performance is optimized by introducing the new up-sampling operator, the attention mechanism, and the feature fusion network. Lastly, the model is trained using knowledge distillation to compensate for accuracy loss resulting from the lightweight design, thereby maintain a higher accuracy. Experimental results based on the Guangdong Power Grid Intelligence Challenge safety belt wearable dataset show that, in the comparison experiments, the improved model, compared with the mainstream object detection algorithm YOU ONLY LOOK ONCE v5s (YOLOv5s), has only 8.7% of the parameters of the former with only 3.7% difference in the mean Average Precision (mAP.50) metrics and the speed is improved by 100.4%. Meanwhile, the ablation experiments show that the improved model’s parameter count is reduced by 66.9% compared with the original model, while mAP.50 decreases by only 1.9%. The overhead safety belt detection model proposed in this paper combines the model’s lightweight design, SimAM attention mechanism, Bidirectional Feature Pyramid Network feature fusion network, Carafe operator, and knowledge distillation training strategy, enabling the model to maintain lightweight and real-time performance while achieving high detection accuracy.

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Acknowledgements

This work is supported by the Hebei Province Graduate Student Innovation Ability Training Funding Project (Grant:CXZZSS2024163) and the Key Research and Development Projects in Hebei Province (Grant:20310103D). The authors would like to thank Mr.Tang from Electric Power Research Institute of Yunnan Electric Power Grid for data support. Special thanks to the Mr.Zhang from The University of Queensland for language support. Special thanks to Ms. Zhang from the Department of Computer of North China Electric Power University for her guidance in the drawing of some of the figure.

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Authors and Affiliations

  1. Department of Computer, North China Electric Power University, Baoding, 071501, China

    Li Liu, Kaiye Huang, Yuang Bai, Qifan Zhang & Yujian Li

  2. Hebei Key Laboratory of Knowledge Computing for Energy and Power, Baoding, 071501, China

    Li Liu, Kaiye Huang, Yuang Bai, Qifan Zhang & Yujian Li

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  1. Li Liu
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Correspondence to Kaiye Huang.

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Liu, L., Huang, K., Bai, Y. et al. Real-time detection model of electrical work safety belt based on lightweight improved YOLOv5. J Real-Time Image Proc 21, 151 (2024). https://doi.org/10.1007/s11554-024-01533-6

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