文献类型：期刊文献

英文题名：Insulator defect detection in complex scenarios based on cascaded networks with lightweight attention mechanism

作者：Yang, Ning[1,2];Li, Xiang[1,2];Jing, Hongyuan[2,3];Shang, Xinna[2,3];Shen, Ping[4];Chen, Aidong[2,3]

第一作者：Yang, Ning

通讯作者：Chen, AD[1];Chen, AD[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China;[4]Zhejiang Changzheng Vocat & Tech Coll, Comp & Informat Technol Sch, Hangzhou 310023, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]corresponding author), Beijing Union Univ, Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China.|[11417]北京联合大学;[1141739]北京联合大学机器人学院;

年份：2024

外文期刊名：PEER-TO-PEER NETWORKING AND APPLICATIONS

收录：;EI(收录号：20241715957044);Scopus(收录号：2-s2.0-85190814843);WOS:【SCI-EXPANDED(收录号:WOS:001205428200003)】；

基金：No Statement Available

语种：英文

外文关键词：UAV detection; Composite insulator; Defect detection; Complex environment target detection

摘要：The power system stands as a crucial infrastructure pivotal to the country's modern economic, security and social development. This paper addresses challenges in insulator fault detection on power transmission towers, leveraging the advancements in unmanned aerial vehicles equipped with target detection methods. We propose a novel method for insulator defect detection based on YOLOv5 (You Only Look Once), aiming to mitigate the issues associated with high missed detection rates. Small insulator faults and the limitation of unmanned aerial vehicle on-board capacity make it difficult to detect comprehensively. Firstly, the cluster analysis was carried out on the training data to obtain 9 kinds of better preset anchors for insulator detection, which improved the accuracy of the model to identify the location of targets. Secondly, the base-model is used to detect the insulator region, and the detection results are input into the sub-model to detect the location of faults, so as to form a cascade model, and make full use of the advantages of the two models to solve the problem of high missed detection rate. Finally, a lightweight attention module combining channel attention module and spatial attention module is added in YOLOv5 to improve the base-model's attention to insulator region and suppress complex background features. Experimental results show that compared with the original model, the average precision of the proposed method for insulator detection is increased by 6.9%, and the missed detection rate of the fault location is 30% lower. Significant improvements in insulator detection performance have been achieved using the method proposed in this paper. It can not only effectively improve the detection accuracy, but also make the missed detection rate lower to meet the requirements of insulator defect detection and fault warning applications in complex environments, which proves that it has a wide range of application prospects in practice, especially in the field of power industry.