期刊
IEEE ACCESS
卷 9, 期 -, 页码 115676-115686出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3105419
关键词
Insulators; Feature extraction; Licenses; Classification algorithms; Training; Rail transportation; Convolution; Convolutional neural network; target detection; railway insulator; fault classification; cascade network
The article proposes a railway insulator fault detection network based on convolutional neural network, which detects faulty insulators from images with high resolution and complex background. By cascading the detection network and the fault classification network, the method reduces the network calculations and improves the accuracy of fault classification.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据