Next Article in Journal
A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack
Previous Article in Journal
Geosynthetic Reinforced Steep Slopes: Current Technology in the United States
Article Menu

Export Article

Open AccessArticle

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
2
Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced Aerocraft (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing 211100, China
3
Guizhou Power Grid Co., Ltd. Institute of Electric Power Science, Guiyang 550002 China
4
Department of Automation, North China Electric Power University, Baoding 071003, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2009; https://doi.org/10.3390/app9102009
Received: 22 March 2019 / Revised: 26 April 2019 / Accepted: 8 May 2019 / Published: 16 May 2019
(This article belongs to the Section Computing and Artificial Intelligence)
  |  
PDF [7457 KB, uploaded 17 May 2019]
  |  

Abstract

Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods. View Full-Text
Keywords: unmanned aerial vehicle; high-voltage transmission line inspection; aerial image; insulator fault detection unmanned aerial vehicle; high-voltage transmission line inspection; aerial image; insulator fault detection
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Han, J.; Yang, Z.; Zhang, Q.; Chen, C.; Li, H.; Lai, S.; Hu, G.; Xu, C.; Xu, H.; Wang, D.; Chen, R. A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection. Appl. Sci. 2019, 9, 2009.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top