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Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images

Authors :
Xinlan Deng
Min He
Jingwen Zheng
Liang Qin
Kaipei Liu
Source :
Drones, Vol 8, Iss 9, p 442 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In natural environments, the connecting bolts of overhead lines and power towers are prone to loosening and missing, posing potential risks to the safe and stable operation of the power system. This paper reviews the challenges in bolt defect detection using power vision technology, with a particular focus on unmanned aerial vehicle (UAV) images. These UAV images offer a cost-effective and flexible solution for detecting bolt defects. However, challenges remain, including missed detection due to the small size of bolts, false detection caused by dense and occluded bolts, and underfitting resulting from imbalanced bolt defect datasets. To address these issues, this paper summarizes solutions that leverage deep learning algorithms. An experimental analysis is conducted on a dataset derived from UAV inspections, comparing the detection characteristics and visualizing the results of various algorithms. The paper also discusses future trends in the application of UAV-based power vision technology for bolt defect detection, providing insights for the advancement of intelligent power inspection.

Details

Language :
English
ISSN :
2504446X
Volume :
8
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Drones
Publication Type :
Academic Journal
Accession number :
edsdoj.4f626914df51410eb31c231bfb1b934b
Document Type :
article
Full Text :
https://doi.org/10.3390/drones8090442