输电线路导地线图像识别算法综述

doi:10.3969/j.issn.1008-0198.2023.03.016

湖南电力 ›› 2023, Vol. 43 ›› Issue (3): 103-108.doi: 10.3969/j.issn.1008-0198.2023.03.016

输电线路导地线图像识别算法综述

刘道生¹, 赖勇耀^1,2, 陈星蓉³, 刘晖²

1.江西理工大学电气工程与自动化学院,江西赣州 341000;
2.国网江西省电力有限公司赣州供电公司,江西赣州 341000;
3.国网江西省电力有限公司宁都县供电公司,江西宁都 342800

收稿日期:2022-12-19 修回日期:2023-01-30 出版日期:2023-06-25 发布日期:2023-06-25
通信作者: 赖勇耀(1995),男,学士,助理工程师,研究方向为电网运行及故障诊断。
作者简介:刘道生(1976),男,教授,博士,主要从事变压器设计、电力设备智能检测与故障诊断研究工作。陈星蓉(1997),硕士,工程师,从事电网设备运维检修工作。刘晖(1985),男,学士,高级工程师、高级技师,从事输电线路运维检修相关工作。
基金资助:
国家自然科学基金资助项目(52167017);江西省自然科学基金资助项目(20224BAB204054)

Survey of Transmission Line and Ground Wire Image Recognition Algorithms

LIU Daosheng¹, LAI Yongyao^1,2, CHEN Xingrong³, LIU Hui²

1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000,China;
2. State Grid Ganzhou Power Supply Company, Ganzhou 341000, China;
3. State Grid Ningdu Power Supply Company,Ningdu 342800,China

Received:2022-12-19 Revised:2023-01-30 Online:2023-06-25 Published:2023-06-25

摘要/Abstract

摘要： 基于图像识别算法可以提高输电线路导地线巡检的效率和可靠性,介绍导地线图像识别算法的研究现状,分析图像预处理、导地线提取和缺陷识别三个关键步骤的主流算法,并对其中的不足之处进行了简要分析,对输电线路导地线图像识别算法的未来研究方向进行了展望。

关键词: 图像识别, 输电线路导地线, 图像预处理, 导地线提取, 缺陷识别

Abstract: Based on the advantages of image recognition algorithm of improving the efficiency and reliability of transmission line inspection, this paper introduces the research status of image recognition algorithm for transmission line and ground wire, analyzes the mainstream algorithms of the three key steps of image preprocessing, transmission line and ground wire extraction and defect recognition, and briefly analyzes the shortcomings of these algorithms.And the same time, the future research directions of transmission line conductor and ground line image recognition algorithms are prospected.

Key words: image recognition, transmission lines and ground wire, image preprocessing, transmission line and ground wire extraction, defect identification

中图分类号:

TM755

刘道生, 赖勇耀, 陈星蓉, 刘晖. 输电线路导地线图像识别算法综述[J]. 湖南电力, 2023, 43(3): 103-108.

LIU Daosheng, LAI Yongyao, CHEN Xingrong, LIU Hui. Survey of Transmission Line and Ground Wire Image Recognition Algorithms[J]. Hunan Electric Power, 2023, 43(3): 103-108.

参考文献

[1]蔡敏.特高压输电线路运行维护技术的研究现状分析[J].湖北电力,2011,35(6):1-6.
[2]赵晓鹏,郭威.无人机图像的输电线异物检测方法研究[J].太原科技大学学报,2021,42(2):104-108.
[3]王万国,张晶晶,韩军,等.基于无人机图像的输电线断股与异物缺陷检测方法[J].计算机应用,2015,35(8):2404-2408.
[4]Li Fu,Lu Songtao. Obstacle detection algorithms for aviation[C]//IEEE 2011 International Conference on Computer Science and Automation Engineering.Shanghai,China,IEEE,2011:710-714.
[5]张利.输电线路缺陷无人机图像识别技术应用[J].新型工业化,2022,12(3):180-181,184.
[6]苏凯第,赵巧娥.基于YOLOv5算法的无人机电力巡检快速图像识别[J].电力科学与工程,2022,38(4):43-48.
[7]魏业文,李梅,解园琳,等.基于改进Faster-RCNN的输电线路巡检图像检测[J].电力工程技术,2022,41(2):171-178.
[8]郝艳捧,张伟勋,梁苇,等.输电线路覆冰导线Mask R-CNN图像识别与分割[J].广东电力,2022,35(2):1-10.
[9]田二胜,李春蕾,朱国栋,等.基于YOLOv4的输电线路外破隐患识别算法[J].计算机系统应用,2021,30(7):190-196.
[10]张鹏宇,李茂,刘建戈,等.电力系统中人工智能图像识别技术的研究分析[J].自动化应用,2021,(10):97-99.
[11]杨冬,章耀辉,张忠.基于图像识别技术的输电线路智能监控系统[J].信息技术,2021,45(9):111-115,120.
[12]罗永超,林韩,卓宸毅,等.基于matlab图像识别的输电线路断股散股检测的研究[J].电子测试,2021, (17):112-113,40.
[13]王芝茗,燕福龙,朱琳琳,等.图像增强算法在高压输电线路巡检中的应用研究[J].东北电力技术,2013,34(4):1-4.
[14]王帅,刘光宇,曹禹,等.改进自适应中值滤波算法的图像去噪研究[J].河南科技学院学报(自然科学版),2022,50(6):43-48.
[15]蔺玉珂,杨敏,贺中屏,等.基于MATLAB的Canny图像边缘检测优化与实现[J].现代信息科技,2022,6(17):81-84.
[16]卞荣,陈科技,张柏岩,等. 基于改进Canny算法的输电导线覆冰冰形视觉识别[J]. 高压电器,2021,57(11):131-138.
[17]李磊,李英娜,赵振刚.基于改进Canny算子的输电导线图像边缘检测[J].电力科学与工程,2021,37(12):19-27.
[18]Lin Yongjian, Liu Kanglin, Wei Baorong, et al. Edge detection of foreign matter suspension image of high voltage transmission line based on improved Canny operator[J]. Journal of Physics: Conference Series,2021,2087(1):012091.
[19]王昊,吴功平,刘中云,等. 基于自适应Canny边缘检测的输电导线检测算法[J]. 扬州大学学报(自然科学版),2020,23(6):45-52.
[20]李玉峰,张宸.一种基于LSD改进的室内直线特征匹配算法[J].现代计算机,2022,28(9):15-21.
[21]王坚俊,章玮,楼华辉,等.基于无人机图像的输电线检测方法[J].计算机系统应用,2019,28(2):233-239.
[22]Li Z R, Liu Y E, Walker R, et al. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform[J]. Machine Vision and Applications, 2010, 21(5): 677-686.
[23]夏斌,张红阳,李冶,等.基于无人机图像的输电线自动提取技术[J].电子器件,2021,44(6):1457-1462.
[24]赵永生,徐海青,吴立刚,等.基于霍夫直线变换的输电线路异物识别应用研究[J].数字技术与应用,2017,(3):127-129,132.
[25]金立军,姚春羽,闫书佳,等.基于航拍图像的输电线路异物识别[J].同济大学学报(自然科学版),2013,41(2):277-281.
[26]Li Zhengrong, Liu Yuee,Hayward Ross, et al. Knowledge-based power line detection for UAV surveillance and inspection systems[C]//IEEE 2008 23rd International Conference Image and Vision Computing.Christchurch, New Zealand,IEEE,2008:1-6.
[27]Cao W R,Zhu L L,Han J D, et al. High voltage transmission line detection for uav based routing inspection[C]// IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Wollongong,Australia,IEEE,2013:554-558.
[28]Sandler M, Howard A, Zhu M L, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]// Proceedings of the IEEE conference on computer vision and pattern recognition.Salt Lake City,UT USA, IEEE,2018:4510-4520.
[29]董召杰.基于YOLOv3的电力线关键部件实时检测[J].电子测量技术,2019,42(23):173-178.
[30]廖如超,张英,廖建东,等.基于语义信息分块的高像素导线缺陷目标识别[J].电力科学与技术学报,2022,37(3):206-212.
[31]冯河清,鹿可可,文裕钧,等.基于航拍图像的输电线路导线损伤识别技术[J].传感技术学报,2022,35(8):1123-1130.
[32]梁新福,罗日成,党世轩,等.基于数字图像处理的电力线异物识别方法研究[J].电气技术,2022,23(2):73-78

输电线路导地线图像识别算法综述

Survey of Transmission Line and Ground Wire Image Recognition Algorithms

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