基于EPSA-YOLOv5电力高空作业安全带佩戴检测OACSTPCD
Safety belt wearing detection for electric aloft work based on EPSA-YOLOv5
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.
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.
李永福;陈立斌;惠君伟;袁润枞;柴浩凯
国网陕西电力有限公司建设分公司,陕西西安 710075国网陕西电力有限公司建设分公司,陕西西安 710075国网陕西电力有限公司建设分公司,陕西西安 710075国网陕西电力有限公司建设分公司,陕西西安 710075西安理工大学电气工程学院,陕西西安 710048
计算机与自动化
安全带检测YOLOv5模型EPSANetSoft-NMS金字塔池化结构
safety belt detectionYOLOv5 modelEPSANetSoft-NMSpyramid pooling struc-ture
《西安工程大学学报》 2024 (2)
18-25,8
国家自然科学基金(52177193)陕西省重点研发计划(2022GY-182)西安市科技计划项目(22GXFW0078)
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