Abstract: Safety protection equipment is an important facility to protect the safety of operators. In order to improve the precision of personnel safety equipment identification and solve the problem of intelligent safety management of fire operations on construction sites. A detection method of safety equipment for personnel in fire operation under deep learning was proposed in this paper. The YOLOv5 network was enhanced by incorporating the CBAM attention mechanism and the SPD Conv module. The feature extraction ability of the network was improved by optimization. A dataset training network was established to develop an intelligent detection model for identifying safety equipment of pyrotechnic operators. The results show that the improved YOLOv5 network model has an average recognition accuracy of 92.9% for the four types of safety equipment, which is an 8.8% improvement over the original network. The method exhibits high recognition accuracy in detecting safety equipment at fire operation sites. It can effectively promote intelligent safety management on construction sites.

Key words: fire operation, deep learning, safety equipment, YOLOv5 network