An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network

doi:10.11930/j.issn.1004-9649.201806102

Abstract

Abstract: 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.

Key words: transmission line, intelligent inspection, insulator, YOLOv2 network, deep learning, image recognition, defect diagnosis

CLC Number:

TM853
TP277

LAI Qiupin, YANG Jun, TAN Bendong, WANG Liang, FU Siyao, HAN Liwei. An Automatic Recognition and Defect Diagnosis Model of Transmission Line Insulator Based on YOLOv2 Network[J]. Electric Power, 2019, 52(7): 31-39.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201806102

https://www.electricpower.com.cn/EN/Y2019/V52/I7/31

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