Railway Insulator Detection Based on Adaptive Cascaded Convolutional Neural Network

Zaixing Wang,Lijun Zheng,Yufan Bao,Xiaozhong Liu,Jinhui Gao,Huayi Peng

doi:10.1109/access.2021.3105419

Zaixing Wang, Lijun Zheng + Show 4 more

Open Access

https://doi.org/10.1109/access.2021.3105419

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Abstract

Insulator failure is one of the important causes of railway power transmission accidents. In the automatic detection system of railway insulators, the detection and classification of insulator faults is a challenging task due to the complex background, small insulators and unobvious failures. In this article, we propose a railway insulator fault detection network based on convolutional neural network, which can detect faulty insulators from images with high resolution and complex background. The insulator fault detection network realizes the position detection and fault classification of the insulator by cascading the detection network and the fault classification network. The method of cascading two networks can reduce the amount of network calculations and improve the accuracy of fault classification. The insulator detection network uses low-resolution images for position detection, and this method can prevent the detection network from paying too much attention to the details of the image, thereby reducing the amount of network calculations. The fault classification network uses high-resolution insulator images for fault classification. The high-resolution images in this method have rich detailed information, which helps to improve the accuracy of fault classification. The trained insulator detection network and the fault classification network are cascaded to form an insulator fault detection network. The precision, recall and mAP values of the insulator fault detection network are 94.10%, 92.88% and 93.46% respectively. Experiment shows show that this network cascading method can significantly improve the accuracy and robustness of insulator fault detection.

Highlights

The insulator is located between the arm and the pillar in the railway catenary, and has been exposed to the atmosphere for a long time; it has to withstand wind and sun, and withstand strong electric field and strong mechanical stress
A fault classification network test experiment was set up to test the performance of the fault classification network
In this paper, we have designed an insulator detection and fault classification network based on deep convolutional neural networks to check the status of insulators in railway catenary images taken by high-definition cameras

Summary

Introduction

The insulator is located between the arm and the pillar in the railway catenary, and has been exposed to the atmosphere for a long time; it has to withstand wind and sun, and withstand strong electric field and strong mechanical stress. The associate editor coordinating the review of this manuscript and approving it for publication was Fanbiao Li. detection is to detect the leakage current of the insulator, and use electrical methods to detect whether the insulator has leakage current. Detection is to detect the leakage current of the insulator, and use electrical methods to detect whether the insulator has leakage current This detection method is susceptible to electromagnetic interference caused by arcs, which affects the accuracy of judgment. The non-electricity detection is based on the image information of the insulator, and the method for processing image is used to detect the faulty insulator. The advantages of this type of detection method are non-contact, fast response and good linearity. The insulator detection method in this article belongs to the non-electricity detection method

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Results

Conclusion