Smart Detection of Safety Conditions of Construction Workers at Construction Sites Based on YOLOv5s Algorithm

Construction site safety has always been the focus of the construction industry, and how to effectively monitor and prevent the safety hazards of construction workers is an urgent problem. With the rapid development of artificial intelligence technology, safety detection based on computer vision has become a feasible solution. In this paper, an intelligent detection method of safety conditions of construction personnel based on YOLOv5s algorithm is proposed. Firstly, the YOLOv5s model is used to detect the targets on the construction site image and identify all kinds of construction workers. Then, based on the position and movement of the construction personnel, we determine whether there are safety hazards, such as whether they are wearing helmets and whether they are engaged in overhead work. Finally, the detection results are fed back to the management personnel to realize the real-time monitoring and early warning of the safety condition of the construction site. Through testing on real construction site data sets, the detection accuracy is between 94% and 99%, and the proposed detection method provides effective technical support for intelligent construction management.