Detection Method for Disconnect and Spread Defects in Overhead Conductors Based on Improved YOLOv5

SHEN Hongdu; SHI Baohua; WEN Bin; LI Yuda

doi:null

您当前的位置：

首页 >

文章列表页 >

Detection Method for Disconnect and Spread Defects in Overhead Conductors Based on Improved YOLOv5

更新时间：2025-06-18

- Detection Method for Disconnect and Spread Defects in Overhead Conductors Based on Improved YOLOv5
- High Voltage Apparatus Pages: 1-12(2025)
- 作者机构：
  
  1.三峡大学电气与新能源学院，湖北　宜昌　 443002
  2.三峡大学湖北省输电线路工程技术研究中心，湖北　宜昌　 443002
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62273200);Hubei Transmission Line Engineering Technology Research Center (Three Gorges University) Open Research Fund Project under Grant(2022KXL03)
- DOI：
  CLC：
- Published：
- 稿件说明：
移动端阅览
SHEN Hongdu, SHI Baohua, WEN Bin, et al. Detection Method for Disconnect and Spread Defects in Overhead Conductors Based on Improved YOLOv5[J/OL]. High voltage apparatus, 2025, 1-12.
DOI：

SHEN Hongdu, SHI Baohua, WEN Bin, et al. Detection Method for Disconnect and Spread Defects in Overhead Conductors Based on Improved YOLOv5[J/OL]. High voltage apparatus, 2025, 1-12. DOI：

摘要

针对现有方法对架空导线断股和散股检测速度较慢、精度较低的问题，提出一种基于YOLOv5改进的增强不典型特征的轻量化网络（enhanced atypical feature lightweight network，EAFYOLO）。首先，对颈部网络进行局部轻量化，提升检测速度；其次，在骨干网络后嵌入一层通道注意力机制保留大权重通道，将重要信息过滤给颈部网络，使颈部网络提取更多有效信息；最后，提出增强不典型特征金字塔网络和高效特征提取网络，有效提升检测精度。通过自制的相关数据集与巡检修补机器人拍摄的图片进行验证，实验结果表明，EAFYOLO算法相较于改进前算法对架空导线断股和散股缺陷检测效果有较大提升。

Abstract

Aiming at the problems of slow speed and low accuracy in detecting disconnect and spread defects of overhead wires using existing methods

an enhanced atypical feature lightweight network (EAFYOLO) based on YOLOv5 improvement is proposed. Firstly

local lightweight is applied to the neck network to improve detection speed; Secondly

a layer of channel attention module is embedded behind the backbone network to retain large weight channels

filtering important information to the neck network and enabling it to extract more effective information；Finally

an enhanced pyramid network of atypical features and an efficient feature extraction network are proposed to effectively improve detection accuracy. Through the verification of self-made relevant datasets and images taken by inspection and repair robots

the experimental results show that the EAFYOLO algorithm significantly improves the detection effect of disconnect and spread defects in overhead wires compared to the previous algorithm.

关键词

Keywords

references

隋宇 , 宁平凡 , 牛萍娟 , 等 . 面向架空输电线路的挂载无人机电力巡检技术研究综述 [J ] . 电网技术 , 2021 , 45 ( 9 ): 3636 - 3648 .

SUI Yu , NING Pingfan , NIU Pingjuan , et al . Review on mounted UAV for transmission line inspection [J ] . Power System Technology , 2021 , 45 ( 9 ): 3636 - 3648 .

翟学明 , 李晓 , 翟羽佳 . 基于深度学习的架空输电导线缺陷检测方法研究 [J ] . 电网技术 , 2023 , 47 ( 3 ): 1022 - 1030 .

ZHAI Xueming , LI Xiao , ZHAI Yujia . Defect detection of overhead transmission wires based on deep learning [J ] . Power System Technology , 2023 , 47 ( 3 ): 1022 - 1030 .

HUANG Xinbo , WU Yiqun , ZHANG Ye , et al . A method of transmission conductor-loosened detect based on image sensors [J ] . IEEE Transactions on Instrumentation and Measurement , 2020 , 69 ( 11 ): 8783 - 8796 .

彭佳鹰 . 架空导线断股修补机器人的设计与同步控制 [D ] . 无锡 : 江南大学 , 2022 .

PENG Jiaying . Design and synchronous control of robot for repairing broken strands of overhead [D ] . Wuxi : Jiangnan University , 2022 .

缪希仁 , 刘志颖 , 鄢齐晨 . 无人机输电线路智能巡检技术综述 [J ] . 福州大学学报(自然科学版) , 2020 , 48 ( 2 ): 198 - 209 .

MIAO Xiren , LIU Zhiying , YAN Qichen . Review on UAV intelligent technology for transmission line inspection [J ] . Journal of Fuzhou University (Natural Science Edition) , 2020 , 48 ( 2 ): 198 - 209 .

赵乐 , 王先培 , 姚鸿泰 , 等 . 基于可见光航拍图像的电力线提取算法综述 [J ] . 电网技术 , 2021 , 45 ( 4 ): 1536 - 1546 .

ZHAO Le , WANG Xianpei , YAO Hongtai , et al . Survey of power line extraction methods based on visible light aerial image [J ] . Power System Technology , 2021 , 45 ( 4 ): 1536 - 1546 .

齐国顺 , 尚方 , 韩冰 , 等 . 基于图像处理技术的飞机巡线导线断股缺陷检测方法 [J ] . 黑龙江电力 , 2017 , 39 ( 6 ): 522 - 526 .

QI Guoshun , SHANG Fang , HAN Bing , et al . Wire breakage defectdetection method in the aircraft inspection lines based on image processing technology [J ] . Heilongjiang Electric Power , 2017 , 39 ( 6 ): 522 - 526 .

刘长银 , 王义春 , 侯艳权 , 等 . 无人机电力巡检中的输电线路断股检测与定位 [J ] . 黑龙江电力 , 2020 , 42 ( 4 ): 314 - 318 .

LIU Changyin , WANG Yichun , HOU Yanquan , et al . Detection and location of broken-strand transmission lines in UAV power inspection [J ] . Heilongjiang Electric Power , 2020 , 42 ( 4 ): 314 - 318 .

FANG Shang , SHENG Liu , XIAOYU Wang . Detection method of transmission line broken stock defects in aircraft inspection based on image processing technology [C ] // 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) . [S.l.] : IEEE , 2020 : 1715 - 1719 .

刘传洋 , 吴一全 . 基于深度学习的输电线路视觉检测方法研究进展 [J ] . 中国电机工程学报 , 2023 , 43 ( 19 ): 7423 - 7445 .

LIU Chuanyang , WU Yiquan . Research progress of vision detection methods based on deep learning for transmission lines [J ] . Proceedings of the CSEE , 2023 , 43 ( 19 ): 7423 - 7445 .

肖志云 , 王海强 . 图像双分割与小波域多特征融合的高压输电线路典型小目标故障识别 [J ] . 电网技术 , 2021 , 45 ( 11 ): 4461 - 4469 .

XIAO Zhiyun , WANG Haiqiang . Typical small target fault identification of high-voltage transmission lines based on image double seg-mentation and fusion of multi-features in wavelet domain [J ] . Power System Technology , 2021 , 45 ( 11 ): 4461 - 4469 .

陈嘉琛 , 俞曜辰 , 陈中 , 等 . 基于改进YOLOv3的输电线路缺陷识别方法 [J ] . 南方电网技术 , 2021 , 15 ( 1 ): 114 - 120 .

CHEN Jiachen , YU Yaochen , CHEN Zhong , et al . An improved method for defect identification of transmission lines based on YOLOv3 [J ] . Southern Power System Technology , 2021 , 15 ( 1 ): 114 - 120 .

李飞伟 , 郁云忠 , 袁林峰 , 等 . 基于无人机的配电导线断股缺陷检测系统设计与实现 [J ] . 测绘地理信息 , 2021 , 46 ( 4 ): 132 - 135 .

LI Feiwei , YU Yunzhong , YUAN Linfeng , et al . Design and implementation of broken strands detection from transmission line images based on UAV [J ] . Journal of Geomatics , 2021 , 46 ( 4 ): 132 - 135 .

王宇博 , 尚军利 , 张烨 , 等 . 基于改进YOLOv7的实时输电导线缺陷检测方法 [J ] . 南方电网技术 , 2023 , 17 ( 12 ): 127 - 134 .

WANG Yubo , SHANG Junli , ZHANG Ye , et al . Real-Time transmission wire defect detection method based on improved YOLOv7 [J ] . Southern Power System Technology , 2023 , 17 ( 12 ): 127 - 134 .

伍箴燎 , 吴正平 , 孙水发 . 基于改进YOLOv5算法的绝缘子多缺陷检测 [J/OL ] . 高压电器 , 1 - 11 [ 2024-08-13 ] . http://kns.cnki.net/kcms/detail/61.1127.TM.20230814.1815.002.html http://kns.cnki.net/kcms/detail/61.1127.TM.20230814.1815.002.html .

WU Zhenliao , WU Zhengping , SUN Shuifa . Multiple Defect Detection of Insulators Based on Improved YOLOv5 Algorithm [J/OL ] . High Voltage Apparatus , 1 - 11 [ 2024-08-13 ] . http://kns.cnki.net/kcms/detail/61.1127.TM.20230814.1815.002.html http://kns.cnki.net/kcms/detail/61.1127.TM.20230814.1815.002.html .

LI, Hulin , LI Jun , WEI Hanbing , et al . Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles [J ] . arXiv , 2022 ( v2 ): 1 - 17 .

HU Jie , SHEN Li , SUN Gang . Squeeze-and-Excitation networks [C ] // Proceedings of the IEEE conference on computer vision and pattern recognition . [S.l.] : IEEE , 2018 : 7132 - 7141 .

FENG Zhiqiang , GUO Li , HUANG Darong , et al . Electrical insulator defects detection method based on YOLOv5 [C ] // 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) . Suzhou, China : IEEE , 2021 : 979 - 984 .

ZHOU Fangbo , ZHAO Huailin , NIE Zhen . Safety helmet detection based on YOLOv5 [C ] // 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) . Shenyang, China : IEEE , 2021 : 6 - 11 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: unified, Real-Time object detection [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 779 - 788 .

LIN T Y , DOLLÁR P , GIRSHICK R , et al . Feature pyramid networks for object detection [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Honolulu, HI, USA : IEEE , 2017 : 936 - 944 .

柯澳 , 王宇聪 . 基于深度学习的目标检测算法在电力巡检上的应用综述 [J ] . 广西电力 , 2022 , 45 ( 6 ): 47 - 56 .

KE Ao , WANG Yucong . A review of the application of deep learning based object detection algorithms in power inspection [J ] . Guangxi Electric Power , 2022 , 45 ( 6 ): 47 - 56 .

邵延华 , 张铎 , 楚红雨 , 等 . 基于深度学习的YOLO目标检测综述 [J ] . 电子与信息学报 , 2022 , 44 ( 10 ): 3697 - 3708 .

SHAO Yanhua , ZHANG Duo , CHU Hongyu , et al . A review of YOLO object detection based on deep learning [J ] . Journal of Electronics & Information Technology , 2022 , 44 ( 10 ): 3697 - 3708 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰