基于深度学习的无人机巡检架空输电线路金具锈蚀缺陷检测方法

doi:10.3969/j.issn.1008-0198.2022.05.013

湖南电力 ›› 2022, Vol. 42 ›› Issue (5): 75-78.doi: 10.3969/j.issn.1008-0198.2022.05.013

基于深度学习的无人机巡检架空输电线路金具锈蚀缺陷检测方法

张家盛¹, 梁进兴²

1.华中科技大学能源与动力工程学院,湖北武汉 430074;
2.南方电网公司桂林供电局,广西桂林 541000

收稿日期:2022-05-09 修回日期:2022-08-03 出版日期:2022-10-25 发布日期:2022-11-16

Detection Method of Metal Fitting Rust Defects for Overhead Transmission Lines Based on UAV Patrol of Deep Learning

ZHANG Jiasheng¹, LIANG Jinxing²

1. School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074 ,China;
2. China Southern Power Grid Company Limited Guilin Power Supply Bureau,Guilin 541000,China

Received:2022-05-09 Revised:2022-08-03 Online:2022-10-25 Published:2022-11-16

摘要/Abstract

摘要： 为了提升架空线路无人机巡检效率,提高架空线路金具锈蚀缺陷智能检测效率,提出了一种基于深度学习的巡检架空线路销钉缺陷检测方法。由于架空输电线路的金具锈蚀缺陷智能检测存在环境背景大、目标小、拍摄角度和拍摄光线差异大等特点,采用图像预处理算法拓充数据集,将MobileNet替换YOLO的主干特征提取网络来提升算法的泛化能力和鲁棒性,并用实际巡检图像进行实验测试。测试集验证中,当置信度阈值取0.5时,P为0.92、R为0.84、A_P为91.34%。结果表明,此方法对架空线路金具锈蚀缺陷有较好的检测效果,可以给设备健康状态评估提供参考。

关键词: 架空输电线路, 深度学习, 无人机巡检, 金具锈蚀检测, 图像处理

Abstract: In order to improve the efficiency of UAV inspection of overhead lines and the intelligent detection efficiency of rust defects of overhead line metal fitting, a deep learning-based inspection method for rust defects of overhead lines metal fitting is proposed. Due to the characteristics of large environmental background, small target, and large difference in shooting angle and shooting light in the intelligent detection of metal fittings rust defects of overhead transmission lines, this paper uses image preprocessing algorithm to expand the data set. And YOLO is replaced by MobileNet′s backbone feature extraction network to improve the generalization ability and robustness of the algorithm, and the actual inspection images are used for experimental testing. In the test set validation, when the confidence threshold is 0.5, the P value is 0.92, the R value is 0.84, and the A_P value is 91.34%. The results show that this method has a good detection effect on the rust defects of overhead line metal fittings, and can provide reference for the assessment of equipment health status.

Key words: overhead transmission lines, deep learning, UAV patrol, metal fitting rust detection, image processing

中图分类号:

TM755

张家盛, 梁进兴. 基于深度学习的无人机巡检架空输电线路金具锈蚀缺陷检测方法[J]. 湖南电力, 2022, 42(5): 75-78.

ZHANG Jiasheng, LIANG Jinxing. Detection Method of Metal Fitting Rust Defects for Overhead Transmission Lines Based on UAV Patrol of Deep Learning[J]. Hunan Electric Power, 2022, 42(5): 75-78.

参考文献

[1]顾超越, 李喆, 史晋涛, 等. 基于改进Faster-RCNN的无人机巡检架空线路销钉缺陷检测 [J]. 高电压技术, 2020, 46(9): 3089-3096.
[2]曹长胜, 张军. 220 kV线路输电铁塔老化锈蚀处理 [J]. 黑龙江电力, 2020, 42(4): 346-348,365.
[3]詹约章. 架空输电线路设施腐蚀的监测方法及预防措施 [J]. 材料保护, 2015, 48(9): 69-70.
[4]蒋光富. 由冷镀锌钢绞线锈蚀断线引起的架空线路保护动作跳闸事故 [J]. 电世界, 2012, 53(12): 39.
[5]刘通, 代洲, 王颂, 等. 架空输电线路的故障模式及相关故障树分析 [J]. 南方电网技术, 2013, 7(6): 108-112.
[6]朱珠. 图像处理技术在架空线路绝缘子破损检测系统中的应用研究 [D]. 镇江:江苏大学, 2008.
[7]刘琦, 翟瑞聪, 曾群生, 等. 架空输电线路均压环缺陷图像智能识别研究 [J]. 电子设计工程, 2022, 30(6): 170-173,178.
[8]WANG C-Y, BOCHKOVSKIY A, LIAO H-Y M, et al. Scaled-YOLOv4: Scaling Cross Stage Partial Network[C]//proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Electr Network, 2021.
[9]王勇, 王永旺, 郭建勋. 基于AI大数据技术的无人机巡线研究 [J]. 电力大数据, 2020, 23(11): 17-23.
[10]王珏飞. 基于深度学习的航拍绝缘子识别及缺损检测方法研究 [D]. 上海:上海电机学院, 2021.
[11]蒋志佳. 图像处理技术在架空线路绝缘子裂缝识别中的应用研究 [D]. 镇江:江苏大学, 2009.
[12]秦帅兵. 基于深度学习的架空线路异物巡检系统设计 [D]. 郑州:郑州大学, 2021.
[13]王刚. 架空输电线路本体缺陷图像自动识别技术 [J]. 电子技术与软件工程, 2019(17): 54-55.
[14]缪希仁, 林志成, 江灏, 等. 基于深度卷积神经网络的输电线路防鸟刺部件识别与故障检测 [J]. 电网技术, 2021, 45(1): 126-133.
[15]廖金, 董国芳, 刘畅. 基于深度学习的架空线路绝缘子掉串识别研究 [J]. 现代电子技术, 2022, 45(2): 167-171.
[16]REDMON J, DIVVALA S, GIRSHICK R, et al. You Only Look Once: Unified, Real-Time Object Detection[C]//proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, 2016.
[17]Andrew G.Howard,Menglong zhu,Bo Chen, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [J]. arxiv:1704.04861[CS.CV], 2017.

基于深度学习的无人机巡检架空输电线路金具锈蚀缺陷检测方法

Detection Method of Metal Fitting Rust Defects for Overhead Transmission Lines Based on UAV Patrol of Deep Learning

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