An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network
Aiming at the transmission line insulator images obtained by drones or robots, this paper proposes an online recognition and defect diagnosis model of transmission line insulators based on YOLOv2 network. The YOLOv2 network is trained to learn and accurately recognize the characteristics of various...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2019-07-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201806102 |
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| _version_ | 1850222034791956480 |
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| author | Qiupin LAI Jun YANG Bendong TAN Liang WANG Siyao FU Liwei HAN |
| author_facet | Qiupin LAI Jun YANG Bendong TAN Liang WANG Siyao FU Liwei HAN |
| author_sort | Qiupin LAI |
| collection | DOAJ |
| description | Aiming at the transmission line insulator images obtained by drones or robots, this paper proposes an online recognition and defect diagnosis model of transmission line insulators based on YOLOv2 network. The YOLOv2 network is trained to learn and accurately recognize the characteristics of various insulators under complicated background, and eventually to accomplish the defect diagnosis of the identified insulators of various status by means of edge detection, line detection, image rotation and vertical projection methods. The simulation results of the patrol inspection images of the transmission lines show that the proposed automatic insulator identification and defect diagnosis method can quickly and accurately identify the insulators from the patrol images of the transmission lines and diagnose the defects and their locations of the insulators, which is beneficial to enhance the intelligence inspection level of transmission lines. |
| format | Article |
| id | doaj-art-08259414b2a146abb2aeba70d64909b5 |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2019-07-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-08259414b2a146abb2aeba70d64909b52025-08-20T02:06:30ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492019-07-01527313910.11930/j.issn.1004-9649.201806102zgdl-52-5-laiqiupinAn Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 NetworkQiupin LAI0Jun YANG1Bendong TAN2Liang WANG3Siyao FU4Liwei HAN5School of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering, Wuhan University, Wuhan 430072, ChinaAiming at the transmission line insulator images obtained by drones or robots, this paper proposes an online recognition and defect diagnosis model of transmission line insulators based on YOLOv2 network. The YOLOv2 network is trained to learn and accurately recognize the characteristics of various insulators under complicated background, and eventually to accomplish the defect diagnosis of the identified insulators of various status by means of edge detection, line detection, image rotation and vertical projection methods. The simulation results of the patrol inspection images of the transmission lines show that the proposed automatic insulator identification and defect diagnosis method can quickly and accurately identify the insulators from the patrol images of the transmission lines and diagnose the defects and their locations of the insulators, which is beneficial to enhance the intelligence inspection level of transmission lines.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201806102transmission lineintelligent inspectioninsulatoryolov2 networkdeep learningimage recognitiondefect diagnosis |
| spellingShingle | Qiupin LAI Jun YANG Bendong TAN Liang WANG Siyao FU Liwei HAN An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network Zhongguo dianli transmission line intelligent inspection insulator yolov2 network deep learning image recognition defect diagnosis |
| title | An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network |
| title_full | An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network |
| title_fullStr | An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network |
| title_full_unstemmed | An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network |
| title_short | An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network |
| title_sort | automatic recognition and defect diagnosis model of transmission line insulator based on yolov2 network |
| topic | transmission line intelligent inspection insulator yolov2 network deep learning image recognition defect diagnosis |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201806102 |
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