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.

Highlights

  • The status detection of electric power equipment is an essential technique for the high-voltage transmission lines inspection in which a wide variety of sensors are used [1]

  • We propose a novel two-step method for insulator faults detection that is based on the CNN feature of unmanned aerial vehicle (UAV) aerial images while considering the unique color and area feature of the insulator faults

  • An accurate and robust method is proposed for insulator faults detection in UAV-based aerial images

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Summary

A Method of Insulator Faults Detection in Aerial

Jiaming Han 1,2 , Zhong Yang 1,2, *, Qiuyan Zhang 3 , Cong Chen 1,2 , Hongchen Li 1,2 , Shangxiang Lai 1,2 , Guoxiong Hu 1,2 , Changliang Xu 1,2 , Hao Xu 1,2 , Di Wang 4 and Rui Chen 4. Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced. Aerocraft (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information

Introduction
Insulator Detection
Model Structure
Training Preparation
Insulator
Data Collection
Experimental Results and Discussion
Analysis of the Proposed Network
Based the observation
The running times and the memory usages of the five
Method
10. Based on theinobservation
Experimental
The proposed are takes also analyzed throughtime the detection all the
Methods
Conclusions and Future Works
Full Text
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