基于改进YOLOv5的电工着装检测方法研究OA

This paper proposes a dressing detection method based on improved YOLOv5 to address the issue of non-standard dressing among working personnel in hydroelectric power plants.This method uses object detection technology to automatically detect whether working personnel are wearing safety helmets and their work clothes are wearing properly.For small object detection such as helmets,a lightweight ECAnet attention mechanism module is embedded on the basis of the YOLOv5 network model to reduce the computational complexity of useless information channels,while ensuring the advantage of YOLOv5 detection speed,the ability to extract small object features is improved.The results show that the accuracy,recall,and mAP@0.5 of the improved module increased by 4.3%,2.1%,and 1.4%respectively.