Abstract:

In order to accurately identify unsafe behaviors of personnel in complex industrial sites and reduce the occurrence of safety accidents, an improved YOLOv5 unsafe behavior detection model was proposed. Firstly, an attention mechanism was introduced in the backbone of YOLOv5 to enhance the sensitivity of convolutional networks to unsafe behavior features. Secondly, enriching the number of training samples through image geometric transformation and pixel-level processing enhanced the generalization ability of the detection model in different industrial environments. Then, the detection model was distilled, and the network structure parameters were optimized to accelerate the training of the mode. Finally, the model was trained and iterated 200 times to simulate three types of industrial sites: lifting slings, robot-automated production lines, and operating rooms. It detected whether personnel were wearing safety helmets, work clothes and working in safe areas, and determined the level of danger based on their behavior to ascertain whether they were working safely. The results show that the model can detect 12 types of unsafe behaviors of personnel in complex industrial environments, such as dim light, lighting, and occlusion. The accuracy on the unsafe behavior test set is 98.6%, the recall rate is 99.2%, and the average accuracy is 97.58%.

Key words: YOLOv5, industrial site, unsafe behavior, detection model, attention module