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
李永福 1陈立斌 1惠君伟 1袁润枞 1柴浩凯2
作者信息
- 1. 国网陕西电力有限公司建设分公司,陕西西安 710075
- 2. 西安理工大学电气工程学院,陕西西安 710048
- 折叠
摘要
电力工作人员在高空作业中,安全带佩戴仍存在漏检和检测速度较慢等现象,利用EPSA-YOLOv5算法,给出了一种新的电力高空作业安全带佩戴检测方法.该方法基于EPSANet的主干抽取网络,在保持良好特征抽取性能的前提下,减少了网络中的参数,加快了模型的辨识速度.通过对空间金字塔池化结构的改进,提高了模型的检测精度,在此基础上,提出了一种基于Soft-NMS的改进算法,以减少对目标的检测.实验结果表明:基于EPSA-YOLOv5网络模型的高空作业安全带检测精度和检测速度等方面均比原YOLOv5模型提高了 2.34％,具有实用性和高效性.
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引用本文复制引用
基金项目
国家自然科学基金(52177193)
陕西省重点研发计划(2022GY-182)
西安市科技计划(22GXFW0078)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据