Dual-Path Attention Network for Multi-State Safety Helmet Identification in Complex Power Scenarios

The environment of the power operation site is complex and changeable, and the accurate identification of the wearing status of workers’ safety helmets is significant to ensure personal safety and the stable operation of the power system. Existing research suffers from high rates of missed detections and limited ability to discriminate fine-grained states, especially the identification of “wrongly wearing” states. Therefore, this paper proposes an intelligent identification method of safety helmet status for power workers based on a dual-path attention network. We embed the convolutional block attention module (CBAM) in the two paths of the backbone and neck layers of YOLOv5 and enhance the feature focusing ability of the key areas of the helmet through the channel-spatial attention coordination, so as to suppress the interference of complex background. In addition, a special dataset covering power scenarios is constructed, including fine-grained state annotation under various lighting, different poses, and occlusion conditions to improve the generalization of the model. Finally, the proposed method is applied to the images of the electric power operation site for experimental verification. The experimental results show that the proposed YOLO-CBAM achieves an outstanding mean average precision of 98.81% for identifying all helmet states, providing reliable technical support for intelligent safety monitoring.