Electric Power ›› 2018, Vol. 51 ›› Issue (11): 139-146.DOI: 10.11930/j.issn.1004-9649.201801094

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Survey of Inspection Technology of Overhead Transmission Line Robot Based on Computer Vision

LI Zhenyu1,2, GUO Rui1,2, LAI Qiupin3, YANG Jun3, YONG Min4, WANG Liang3, FU Siyao3   

  1. 1. Shandong Electric Power Company Research Institute of State Grid of China, Jinan 250003, China;
    2. Shandong Luneng Intelligent Technology Co., Ltd., Jinan 250014, China;
    3. School of Electrical Engineering Wuhan University, Wuhan 430072, China;
    4. Licheng District Power Supply Company of Jinan City of State Grid of China, Jinan 250100, China
  • Received:2018-01-09 Revised:2018-03-26 Online:2018-11-05 Published:2018-11-16
  • Supported by:
    This work is supported by National Natural Science Foundation Project (No.51277135); and Science and Technology Project of SGCC (Research and Application of Key Technologies for Inspection Robots Overhead Transmission Line, No.52600160004).

Abstract: Overhead transmission lines are the key to safe operation of power grid and reliable transmission of electric power, and it is very important to carry out regular inspection. The computer vision system has the characteristics of high integration, good interactivity, high degree of automation and fast processing speed, it can play an important role in the accurate identification and fault judgment of all kinds of equipment during the inspection process of overhead transmission lines, therefore, the computer vision system has a wide range of applications in the overhead transmission line robot inspection and fault diagnosis. In this paper, the robot computer vision inspection technology and its research status are summarized from the aspects of wire identification, tower identification and insulator string identification, the corresponding image processing methods are analyzed, on this basis, the overhead transmission line robot visual inspection key technology has been summarized and foreseen.

Key words: computer vision, overhead transmission line, robot inspection technology, machine learning, image processing, device identification

CLC Number: