无人机航拍图像中绝缘子缺陷检测的深度学习方法研究进展

doi:10.19595/j.cnki.1000-6753.tces.240610

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1497 KB)
输出: BibTeX | EndNote (RIS)

摘要

依托计算机视觉和深度学习技术,从海量的无人机航拍图像中实现绝缘子缺陷检测,已经成为电力运维工作亟待解决的问题。近年来,深度学习方法在绝缘子缺陷检测任务中表现出了优异的性能。该文综述了无人机航拍图像中绝缘子缺陷检测的深度学习方法研究进展。首先,简述了基于深度学习的输电线路巡检研究现状;其次,阐述了基于深度学习的绝缘子缺陷检测方法,主要从目标检测模型、轻量化网络模型、级联检测模型以及其他方法进行归纳总结,按照深度学习算法的发展历程,阐明了基于双阶段、单阶段、Anchor-free的目标检测算法,概述了轻量化网络、级联检测在绝缘子缺陷检测中的应用;然后,介绍了用于绝缘子缺陷检测的公开和自建数据集;最后,指出了深度学习技术在绝缘子缺陷检测应用中存在的问题,并对未来研究工作进行了展望。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘传洋
	吴一全
	刘景景

关键词 ：绝缘子缺陷检测, 无人机航拍图像, 深度学习, 计算机视觉

Abstract：

Insulator is one of the most common and widely used electrical components in transmission lines, which plays a critical role in electrical insulation and mechanical support, ensuring that the current flows along the specified path and reducing electromagnetic interference with the surrounding environment. Since insulators are installed outdoors, they are exposed to wind, sunlight, rain, ice, frost and other bad weather for a long time, and their surface defects are inevitable. If the insulator appears self-explosion or drop string, which will cause leakage due to the loss of insulation, leading to electric shock accidents, thus resulting in huge economic losses. Relying on computer vision and deep learning technology, insulator defect detection from massive UAV aerial images has become an urgent problem for power operation and maintenance. However, the backgrounds of aerial images from overhead transmission line corridors are complex. Under different lighting conditions, shooting angles, shooting distances, etc., the scale of insulators in aerial images varies greatly, and the insulator strings are prone to occlusion, the defect area of the insulator is much smaller than the insulator itself. Therefore, there are numerous difficulties in detecting insulator defects in practical applications.
In recent years, compared with the traditional object detection methods, deep learning methods can quickly and accurately identify insulators and their defects from power inspection images. There is still a lack of comprehensive review of the latest progress in insulator defect detection in existing literature, without introducing object detection algorithms such as anchor free algorithm, YOLOv7, Transformer, and knowledge extraction techniques. In view of this, this article summarizes and analyzes a large number of visual methods for insulator defects detection, systematically reviews deep learning methods for insulator defect detection in drone aerial images, aiming to select appropriate detection methods for specific insulator defects and provide valuable reference for researchers engaged in transmission lines fault diagnosis.
This paper reviews the research progress of deep learning methods for insulator defect detection in UAV aerial images. Firstly, the current research status of transmission lines inspection based on deep learning is briefly reviewed. Then, the insulator defect detection methods based on deep learning are explained, mainly from the target detection models, lightweight network models, cascade detection models and other methods are summarized, which is conducive to the comparison between different deep learning methods and more helpful for power inspection personnel to select appropriate deep vision detection methods for fault diagnosis of specific electrical component. And the target detection models based on two-stage algorithms, one-stage algorithms and anchor-free algorithms are elucidated. The lightweight network models based on model pruning, knowledge distillation, low-rank decomposition, network quantization and the target detection model based on Transformer are summarized. Next, the self-built and public datasets for insulator defect detection are introduced. Due to the lack of training samples and unified dataset for insulator defect detection, scholars mostly conduct defect detection research through self-built datasets in different detection scenarios. Finally, the challenges faced by insulator defect detection methods based on deep learning are elucidated, including insufficient defect samples, low defect detection accuracy, difficulty in detecting small target defects, and feature extraction, etc. Based on existing deep learning techniques and recent research ideas, several important research directions in the future are pointed out, including expanding insulator defect samples, establishing unified performance evaluation indicators, small and zero sample learning, new defect detection frameworks, multi-level detection of small defects, deep fusion of multiple learning technologies, cloud-edge-end collaborative fusion, and improving network model stability and real-time performance.

Key words： Defect detection of insulator UAV based aerial image deep learning computer vision

收稿日期: 2024-04-22

PACS:	TM76
	TP277

基金资助:

国家自然科学基金项目（61573183）和安徽省高校自然科学研究项目（2023AH052358, CZ2022ZRZ07）资助

通讯作者: 吴一全男,1963年生,教授,博士生导师,研究方向为视觉检测与图像测量、图像处理与分析、红外目标检测与识别、视频处理与智能分析等。E-mail：nuaaimage@163.com

作者简介: 刘传洋男,1982年生,博士,研究方向为图像处理与机器视觉,输电线路视觉检测与故障诊断。E-mail：liuchuanyang608@nuaa.edu.cn

引用本文:

刘传洋, 吴一全, 刘景景. 无人机航拍图像中绝缘子缺陷检测的深度学习方法研究进展[J]. 电工技术学报, 0, (): 2492937-2492937. Liu Chuanyang, Wu Yiquan, Liu Jingjing. Research Progress of Deep Learning Methods for Insulator Defect Detection in UAV based Aerial Images. Transactions of China Electrotechnical Society, 0, (): 2492937-2492937.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.240610 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/2492937