Detection Algorithm for Insulators and Foreign Objects on Transmission Lines Based on Unmanned Aerial Vehicle Inspection

YU Zihan; WANG Heming; WANG Jiankai; ZHU Shengqiang; MENG Xiangzhong

doi:10.12096/j.2096-4528.pgt.24230

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Detection Algorithm for Insulators and Foreign Objects on Transmission Lines Based on Unmanned Aerial Vehicle Inspection

Application of AI in New Power System | 更新时间：2026-06-29

- Detection Algorithm for Insulators and Foreign Objects on Transmission Lines Based on Unmanned Aerial Vehicle Inspection
- Power Generation Technology Vol. 46, Issue 3, Pages: 532-540(2025)
- 作者机构：
  
  1.山东黄金电力有限公司，山东省烟台市 266040
  2.青岛科技大学自动化与电子工程学院，山东省青岛市 266061
- 作者简介：
- 基金信息：
  
  Shandong Provincial Natural Science Foundation(ZR2022ME194)
- DOI：10.12096/j.2096-4528.pgt.24230
  CLC： TM 216;TP 391
- Received：29 October 2024，
  
  Revised：2024-11-29，
  
  Published：30 June 2025
- 稿件说明：
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于子涵,王赫鸣,王建凯等.基于无人机巡检的输电线路绝缘子及其异物检测算法[J].发电技术,2025,46(03):532-540.

YU Zihan,WANG Heming,WANG Jiankai,et al.Detection Algorithm for Insulators and Foreign Objects on Transmission Lines Based on Unmanned Aerial Vehicle Inspection[J].Power Generation Technology,2025,46(03):532-540.
于子涵,王赫鸣,王建凯等.基于无人机巡检的输电线路绝缘子及其异物检测算法[J].发电技术,2025,46(03):532-540. DOI： 10.12096/j.2096-4528.pgt.24230.

YU Zihan,WANG Heming,WANG Jiankai,et al.Detection Algorithm for Insulators and Foreign Objects on Transmission Lines Based on Unmanned Aerial Vehicle Inspection[J].Power Generation Technology,2025,46(03):532-540. DOI： 10.12096/j.2096-4528.pgt.24230.

摘要

目的

传统的电网巡检方式存在劳动强度大、效率低等问题，为此，以山东黄金电网为研究对象，提出一种基于轻量化深度学习网络YOLOv5-Mv3的无人机巡检电网绝缘子及其异物检测算法。

方法

首先，通过无人机巡检电网拍摄图片来构建数据集，对数据集进行训练；其次，针对电网绝缘子及其异物，采用Mobilenetv3来替代CSPDarknet53作为特征提取网络，对YOLOv5-Mv3进行轻量化改进，减少模型参数和计算量，在保证准确率的同时，满足实时检测的要求。

结果

该检测算法的均值平均精度达到84.7%，检测速度达到56.6帧/s，改进的YOLOv5-Mv3与Faster RCNN、SSD、YOLOv4模型相比，具有更高的检测精度和更快的检测速度。

结论

该算法提高了无人机巡检电网效率，实现了轻量高效的效果，符合电网智能巡检的要求。

Abstract

Objectives

Traditional power grid inspection methods suffer from high labor intensity and low efficiency. Taking Shandong Golden Power Grid as the research object

this study proposes an inspection algorithm using unmanned aerial vehicle (UAV) based on lightweight deep learning network YOLOv5-Mv3 for detecting grid insulators and foreign objects.

Methods

Firstly

a dataset is constructed using images captured by UAVs during power grid inspection and is trained. Then

for the grid insulators and foreign objects

Mobilenetv3 is used to replace CSPDarknet53 as the feature extraction network in order to lighten the YOLOv5-Mv3 model

reducing parameters and computational cost while maintaining accuracy and enabling real-time detection.

Results

The proposed detection algorithm achieves a mean Average Precision of 84.7% and 56.6 frames per second. Compared to Faster RCNN

SSD

and YOLOv4 models

the improved YOLOv5-Mv3 demonstrates higher detection accuracy and faster performance.

Conclusions

The proposed algorithm improves the efficiency of UAV-based power grid inspection and achieves lightweight and high-efficiency effect

fully meeting the requirements for intelligent power grid inspection.

关键词

Keywords

references

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