Anomaly Detection of Railway Catenary Based on Deep Convolutional Generative Adversarial Networks

Abstract

In the image-based detection of catenary anomalies, detection of bird's nest anomalies is a typical situation. However, the image data containing the nests is only a small portion of total data, which makes nest detection a typical problem of imbalanced data classification. For using machine learning algorithm to solve imbalanced data classification, the learning ability of data features is of much importance. The generative adversarial networks (GANs) can learn prosperous data features from unlabeled data, which has been widely confirmed and applied. In this paper, GANs is used to detect the bird's nest anomalies, which achieved a higher accuracy compared with the traditional method based on feature extraction.