A GAN-Based Anomaly Detection Method for Isoelectric Line in High-Speed Railway

Abstract

The isoelectric line is an important component of the catenary support device in high-speed railway. Isoelectric line is prone to failure during the long-term operation of vehicles. Traditional image processing methods and deep convolution neural networks (DCNNs) classifier cannot obtain good anomaly detection results due to the diverse shapes of isoelectric lines and the lack of fault samples. In this paper, a novel approach is proposed to detect the faulty states of isoelectric lines based on generative adversarial networks (GANs). First, the faster R-CNN (ResNet101) is adopted to extract the isoelectric line features and localize the image areas of isoelectric lines accurately. Second, the generative adversarial representation of isoelectric lines from the first stage is acquired by a deep convolutional generative adversarial network (DCGAN). Finally, an anomaly rating criterion is proposed to distinguish the faulty states of isoelectric line. Experiments and comparisons show that the proposed method can judge faulty isoelectric line correctly. Furthermore, compared with the current typical anomaly detection methods, the proposed method has better performance and robustness for the isoelectric line anomaly detection.