Abstract
The acceptance of railway systems as a frontier transportation infrastructure can be attributed to their reliability, safety, and support for green technology. With the recent advances in artificial intelligence and machine learning (AI/ML), the maintenance of railroad transportation systems has taken a different direction, especially in the analysis of railroad big data, leading to real-time processing and detection of railway problems. However, using limited track data may result in overfitting, hindering the accurate implementation of robust models. In this paper, the authors consider generative adversarial networks (GANs) with keen consideration for possible covariate shifts to improve track defect detection and decrease data imbalance. The results show that implementing covariate-shift GAN (COGAN) reduces image processing time and eliminates image biases.
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