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

ZHANG Jiasheng; LIANG Jinxing

doi:10.3969/j.issn.1008-0198.2022.05.013

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

更新时间：2025-06-11

- Detection Method of Metal Fitting Rust Defects for Overhead Transmission Lines Based on UAV Patrol of Deep Learning
- Vol. 42, Issue 5, Pages: 75-78(2022)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2022.05.013
  CLC： TM755
- Published：2022
- 稿件说明：
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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]. 2022, 42(5): 75-78.
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. DOI： 10.3969/j.issn.1008-0198.2022.05.013.

ZHANG Jiasheng, LIANG Jinxing. Detection Method of Metal Fitting Rust Defects for Overhead Transmission Lines Based on UAV Patrol of Deep Learning[J]. 2022, 42(5): 75-78. DOI： 10.3969/j.issn.1008-0198.2022.05.013.

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

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。

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

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.

关键词

Keywords

references

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