摘要
采用无人机对输电线路进行智能巡检作业已成为行业主流,绝缘子缺陷检测是智能巡检作业中的关键环节。针对复杂环境中绝缘子缺陷检测精度低的问题,本文提出一种基于改进YOLOv5s绝缘子缺陷检测算法。首先,对现有数据集利用随机矩形遮挡、水平翻转、随机像素置零、添加随机像素等操作进行数据增强,并利用K-means算法对数据集进行聚类分析,得到最佳锚框尺寸,有效提高模型的泛化能力和定位精度;其次,在YOLOv5s的主干网络的末端和最后3个不同规模的卷积网络后加入GAM注意力模块,使模型可以在更大的网络上进行注意,来解决无效特征对识别精度的影响;最后,在特征金字塔结构FPN的基础上,引入自适应特征融合ASFF模块,来增强网络的特征提取能力。实验结果表明,改进后YOLOv5s模型的精确率和mAP0.5相比于原YOLOv5s网络分别提高了2.4%和2.2%。
The use of unmanned aerial vehicles for intelligent inspection of transmission lines has become the mainstream of the industry.Insulator defect detection is a key link in intelligent inspection operations.Aiming at the problem of low accuracy of insulator defect detection in complex environment,this paper proposes an improved YOLOv5s insulator defect detection algorithm.Firstly,the existing data sets are enhanced by random rectangle occlusion,horizontal flip,random pixel zeroing and adding random pixels,and the K-means algorithm is used to cluster the data sets to obtain the optimal anchor frame size,which effectively improves the generalization ability and positioning accuracy of the model.Secondly,GAM attention module is added to the end of the main network of YOLOv5s and the last three convolution networks of different scales,so that the model can be noticed on a larger network to solve the influence of invalid features on the recognition accuracy.Finally,based on the feature pyramid structure FPN,the adaptive feature fusion ASFF module is introduced to enhance the feature extraction ability of the network.The experimental results show that the accuracy and mAP0.5 of the improved YOLOv5s model are 2.4%and 2.2%higher than those of the original YOLOv5s network,respectively.
作者
肖粲俊
潘睿志
李超
黄纪刚
Xiao Canjun;Pan Ruizhi;Li Chao;Huang Jigang(Digital Twin Laboratory,Chengdu Technological University,Chengdu 610031,China;College of Mechanical and Electrical Engineering,Chengdu University of Technology,Chengdu 610059,China;School of Mechanical Engineering,Sichuan University,Chengdu 610065,China)
出处
《电子测量技术》
北大核心
2022年第24期137-144,共8页
Electronic Measurement Technology
基金
四川省科技计划项目(2022YFG0326)资助
