Unsafe behavior real-time detection method of intelligent workshop workers based on improved YOLOv5s

Production safety is the basic requirement of human-oriented intelligent manufacturing. To meet the realtime detection and edge deployment requirements of unsafe behaviors of workers in intelligent workshops, a lightweight YOLOv5s-based method for detecting unsafe behaviors of workers in intelligent workshops was proposed. Structural optimizations were deleted on the feature fusion network and output layer of YOLOv5s. The resulting model files after improvement were subjected to structured pruning. The knowledge distillation was applied to fine-tune the pruned network model. Experimental results demonstrated that the improved YOLOv5s algorithm achieved a mAP@0. 5 of up to 97. 8%, a 108% improvement in FPS and requires computational powers decreases by 69. 0%. The proposed YOLOv5s-2Detect network and lightweight design scheme manifested high accuracy, real-time performance, and robustness in detecting unsafe behaviors of workers, thereby satisfying the detection needs of unsafe behaviors of workers in the practical environment of intelligent workshops. © 2024 CIMS. All rights reserved.