Behavior recognition system for construction workers in oil depots failing to wear protective clothing based on computer vision
This paper proposes a behavior recognition system for oil depot construction personnel not wearing uniforms based on computer vision technology.The system adopts a cascaded detector combining YOLOv5s and ResNet-50,and achieves precise detection of non-uniform wearing behavior through the construction of a rich dataset of oil depot construction personnel and experimental verification.Experimental results show that the ResNet-50 model exhibits high accuracy on the test set,providing strong technical support for the safe production management of oil depots.Compared to SqueezeNet,ResNet-50 performs better in recognition accuracy,while the latter excels in memory usage and inference speed.The research in this paper not only helps to improve the intelligent level of safety management in oil depots and reduce safety risks caused by human factors,but also provides useful references and lessons for safety monitoring in similar scenarios.With the continuous development of computer vision technology,this system is expected to play a more important role in the safe production management of oil depots.
computer visionoil depotprotective clothing recognitionsafe production
万方数据
维普
