Ming Mao, Lu Liu, Wenxiang Chen, Weiqi Xiong, Xuelei Xi, Guoqiang Zhu, Yan Zhang, Shuang Wang, Yu Chen
{"title":"基于双目特征融合和改进FCOS检测头的输电线路缺陷识别方法","authors":"Ming Mao, Lu Liu, Wenxiang Chen, Weiqi Xiong, Xuelei Xi, Guoqiang Zhu, Yan Zhang, Shuang Wang, Yu Chen","doi":"10.1109/ICSMD57530.2022.10058247","DOIUrl":null,"url":null,"abstract":"UAVs are widely used in transmission line inspection. Inspectors operate UAVs to take images of transmission lines and analyze and identify these images. Detecting transmission line defects in UAV inspection images is an important task. This paper proposes a transmission line defect detection method based on binocular feature fusion and an improved FCOS detection head. First, a binocular feature fusion module is designed. Second, a feature screening module is added to the network. Finally, add the IoU prediction branch to the FCOS detection head. The experimental results show that the transmission line defect detection method proposed in this paper can effectively identify the two defects of broken strands and foreign objects, and the mAP reaches 90.85%.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Power Transmission Line Defect Recognition Method Based on Binocular Feature Fusion and Improved FCOS Detection Head\",\"authors\":\"Ming Mao, Lu Liu, Wenxiang Chen, Weiqi Xiong, Xuelei Xi, Guoqiang Zhu, Yan Zhang, Shuang Wang, Yu Chen\",\"doi\":\"10.1109/ICSMD57530.2022.10058247\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"UAVs are widely used in transmission line inspection. Inspectors operate UAVs to take images of transmission lines and analyze and identify these images. Detecting transmission line defects in UAV inspection images is an important task. This paper proposes a transmission line defect detection method based on binocular feature fusion and an improved FCOS detection head. First, a binocular feature fusion module is designed. Second, a feature screening module is added to the network. Finally, add the IoU prediction branch to the FCOS detection head. The experimental results show that the transmission line defect detection method proposed in this paper can effectively identify the two defects of broken strands and foreign objects, and the mAP reaches 90.85%.\",\"PeriodicalId\":396735,\"journal\":{\"name\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSMD57530.2022.10058247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSMD57530.2022.10058247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power Transmission Line Defect Recognition Method Based on Binocular Feature Fusion and Improved FCOS Detection Head
UAVs are widely used in transmission line inspection. Inspectors operate UAVs to take images of transmission lines and analyze and identify these images. Detecting transmission line defects in UAV inspection images is an important task. This paper proposes a transmission line defect detection method based on binocular feature fusion and an improved FCOS detection head. First, a binocular feature fusion module is designed. Second, a feature screening module is added to the network. Finally, add the IoU prediction branch to the FCOS detection head. The experimental results show that the transmission line defect detection method proposed in this paper can effectively identify the two defects of broken strands and foreign objects, and the mAP reaches 90.85%.