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

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.