202405020输电线路绝缘子多类型缺陷检测研究

王强; 冯云涛; 周超

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202405020输电线路绝缘子多类型缺陷检测研究

更新时间：2025-06-11

- 202405020输电线路绝缘子多类型缺陷检测研究
- Research on Multi Type Defect Detection of Transmission Line Insulators Based on GE-YOLO Algorithm
- 默认刊物名称 2025年
- 作者机构：
  
  1.智慧能源技术湖北省工程研究中心,湖北省宜昌市443002
  2.三峡大学电气与新能源学院,湖北省宜昌市443002
- 作者简介：
  
  [ "王强（1974—）男，硕士研究生，副教授，研究方向电力系统电力电子装置、新能源发电及并网技术、工业智能检测及控制（）。" ]
  [ "冯云涛（1999—）男，通信作者，硕士研究生，研究方向电力系统设备检测、图像处理（）。" ]
  [ "周超（1997—）男，硕士研究生，研究方向电力系统（）。" ]
- 基金信息：
  
  国家自然科学基金资助项目(52077120)
- DOI：
  中图分类号：
- 收稿日期：2024-09-03，
  
  修回日期：2024-10-22，
  
  录用日期：2024-10-22，
- 稿件说明：
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王强, 冯云涛, 周超. 202405020输电线路绝缘子多类型缺陷检测研究[J/OL]. 默认刊物名称, 2025.

WANG Qiang, FENG Yuntao, ZHOU Chao. Research on Multi Type Defect Detection of Transmission Line Insulators Based on GE-YOLO Algorithm[J/OL]. Moren journal, 2025.
王强, 冯云涛, 周超. 202405020输电线路绝缘子多类型缺陷检测研究[J/OL]. 默认刊物名称, 2025. DOI：

WANG Qiang, FENG Yuntao, ZHOU Chao. Research on Multi Type Defect Detection of Transmission Line Insulators Based on GE-YOLO Algorithm[J/OL]. Moren journal, 2025. DOI：

摘要

因线路绝缘子航拍图像目标尺寸较小、背景复杂等客观局限，致使传统检测算法及多数轻量网络难以准确识别目标缺陷。本文提出一种基于GE-YOLO的绝缘子多类型缺陷检测模型，该算法以YOLOv8s为基础、结合分组卷积和多尺度融合思想设计分组多尺度卷积（Grouping Multi-Scale Convolution

GMSC）并使用其对C2f进行改进，增强模型特征提取能力；在特征融合网络引入高效多尺度注意力机制（Efficient Multi-Scale Attention

EMA），提高模型目标定位能力；最后将损失函数替换为Wise-IOU以降低几何因素的过度惩罚，提高模型回归的性能。实验结果表明，优化后的网络模型计算复杂度基本不变，满足边缘算力需求，整体mAP提升5.5%，其中破损绝缘子mAP提升10.9%，本文算法能够更均衡地实现对绝缘子多类型缺陷的检测。

Abstract

Due to objective limitations such as small target size and complex background in aerial images of line insulators

traditional detection algorithms and most lightweight networks are difficult to accurately identify target defects. This article proposes a multi type defect detection model for insulators based on GE-YOLO. The algorithm is based on YOLOv8s and combines grouping convolution and multi-scale fusion ideas to design Grouping Multi Scale Convolution (GMSC)

which is used to improve C2f and enhance the model's feature extraction ability; Introducing an efficient multi-scale attention mechanism (EMA) into the feature fusion network to improve the model's target localization ability; Finally

replace the loss function with Wise IOU to reduce the excessive penalty of geometric factors and improve the performance of model regression. The experimental results show that the computational complexity of the optimized network model remains basically unchanged

meeting the edge computing power requirements. The overall mAP is increased by 5.5%

among which the mAP of damaged insulators is increased by 10.9%. This algorithm can achieve more balanced and low detection of multiple types of defects in insulators.

关键词

Keywords

references

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