An Unsafe Behavior Detection Method Based on Improved YOLO Framework

Abstract

In industrial production, accidents caused by the unsafe behavior of operators often bring serious economic losses. Therefore, how to use artificial intelligence technology to monitor the unsafe behavior of operators in a production area in real time has become a research topic of great concern. Based on the YOLOv5 framework, this paper proposes an improved YOLO network to detect unsafe behaviors such as not wearing safety helmets and smoking in industrial places. First, the proposed network uses a novel adaptive self-attention embedding (ASAE) model to improve the backbone network and reduce the loss of context information in the high-level feature map by reducing the number of feature channels. Second, a new weighted feature pyramid network (WFPN) module is used to replace the original enhanced feature-extraction network PANet to alleviate the loss of feature information caused by too many network layers. Finally, the experimental results on the self-constructed behavior dataset show that the proposed framework has higher detection accuracy than traditional methods. The average detection accuracy of smoking increased by 3.3%, and the average detection accuracy of not wearing a helmet increased by 3.1%.