Safety belt wearing detection for electric aloft work based on EPSA-YOLOv5
To address the problem of missed detection and slow detection speed in safety belt wearing test for electric aloft work,this paper proposed a method for detecting the wearing of safety belts based on EPSA-YOLOv5 algorithm.This method was based on EPSANet backbone feature extraction network,which reduced the number of parameters in the network while main-taining good feature extraction performance,and speeding up the model recognition speed.By im-proving the spatial pyramid pooling structure,the model detection accuracy was improved;on this basis,an improved algorithm based on Soft-NMS was proposed to reduce the detection of targets.Experimental results show that the detection accuracy and speed of safety belt for aloft work based on EPSA-YOLOv5 network model are 2.34％higher than that of the original YOLOv5 model,which has practicality and efficiency.
safety belt detectionYOLOv5 modelEPSANetSoft-NMSpyramid pooling struc-ture
国家科技期刊平台
NSTL
万方数据
维普
