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Volume 8
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Review

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

by
Xinlan Deng
1,2,
Min He
1,2,
Jingwen Zheng
3,
Liang Qin
1,2,* and
Kaipei Liu
1,2
1
Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, China
2
School of Electrical and Automation, Wuhan University, Wuhan 430072, China
3
Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China
*
Author to whom correspondence should be addressed.
Drones 2024, 8(9), 442; https://doi.org/10.3390/drones8090442
Submission received: 2 August 2024 / Revised: 21 August 2024 / Accepted: 25 August 2024 / Published: 29 August 2024
(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones, 2nd Edition)
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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.
Keywords: power vision technology; UAV inspection; object detection; intelligent recognition; overhead lines; bolt defects power vision technology; UAV inspection; object detection; intelligent recognition; overhead lines; bolt defects

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MDPI and ACS Style

Deng, X.; He, M.; Zheng, J.; Qin, L.; Liu, K. Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images. Drones 2024, 8, 442. https://doi.org/10.3390/drones8090442

AMA Style

Deng X, He M, Zheng J, Qin L, Liu K. Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images. Drones. 2024; 8(9):442. https://doi.org/10.3390/drones8090442

Chicago/Turabian Style

Deng, Xinlan, Min He, Jingwen Zheng, Liang Qin, and Kaipei Liu. 2024. "Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images" Drones 8, no. 9: 442. https://doi.org/10.3390/drones8090442

APA Style

Deng, X., He, M., Zheng, J., Qin, L., & Liu, K. (2024). Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images. Drones, 8(9), 442. https://doi.org/10.3390/drones8090442

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