西安工程大学学报2024，Vol.38Issue(2)：18-25,8.DOI:10.13338/j.issn.1674-649x.2024.02.003
基于EPSA-YOLOv5电力高空作业安全带佩戴检测
Safety belt wearing detection for electric aloft work based on EPSA-YOLOv5
摘要
Abstract
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.关键词
安全带检测/YOLOv5模型/EPSANet/Soft-NMS/金字塔池化结构Key words
safety belt detection/YOLOv5 model/EPSANet/Soft-NMS/pyramid pooling struc-ture分类
信息技术与安全科学引用本文复制引用
李永福,陈立斌,惠君伟,袁润枞,柴浩凯..基于EPSA-YOLOv5电力高空作业安全带佩戴检测[J].西安工程大学学报,2024,38(2):18-25,8.基金项目
国家自然科学基金(52177193) （52177193）
陕西省重点研发计划(2022GY-182) （2022GY-182）
西安市科技计划项目(22GXFW0078) （22GXFW0078）
