A Multiple Species Railway Defects Detection Method Based on Sample Generation

In this article, we focus on constructing a multicategory railway defect detection method, which is important in both railway operation and railway maintenance. We designed a deep learning-based railway defect detection system that includes railway classification, switch spacing measurement, and railway defect sample expansion based on generative adversarial networks (GANs) and railway defect detection networks. Deep learning can build fast and accurate defect detection networks; however, its application in railway scenarios is limited due to the scarcity of defect samples and usually focuses on single-type defect detection. We build a railway defect detection system by balancing positive and negative samples, contactless switch spacing measurements, generating railway defect samples, and transfer learning. We conduct experiments to show that such an approach can be well applied to real railway detection, which greatly solves the problem of low sample size and low generalization of deep learning in railway scenarios. Furthermore, due to our method does not have any requirements for the types and scenarios of railways, most deep learning-based railway defect detection methods can be improved based on our method, which can reduce the difficulty of applying deep learning to railway defect detection. We hope this can promote the research of railway defect detection.