Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection

Recent advancements in cognitive computing, through the integration of artificial intelligence (AI) techniques, have facilitated the development of intelligent cognitive systems (ICS). This benefits railway defect detection by enabling ICS to emulate human-like analysis of defect patterns in image data. Although visual defect classification based on convolutional neural networks (CNN) has achieved decent performance, the scarcity of large datasets for railway defect detection remains a challenge. This scarcity stems from the infrequent nature of accidents that result in defective railway parts. Existing research efforts have addressed the challenge of data scarcity by exploring rule-based and generative data augmentation approaches. Among these approaches, variational autoencoder (VAE) models can generate realistic data without the need for extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for training railway defect classifiers. Our approach introduces a modified regularization strategy that combines weight decay with reconstruction loss. Using this method, we created a synthetic dataset for the Canadian Pacific Railway (CPR), consisting of 50 real samples across five classes. Remarkably, our method generated 500 synthetic samples, achieving a minimal reconstruction loss of 0.021. A visual transformer (ViT) model, fine-tuned using this synthetic CPR dataset, achieved high accuracy rates (98–99%) in classifying the five railway defect classes. This research presents an approach that addresses the data scarcity issue in railway defect detection, indicating a path toward enhancing the development of ICS in this field.