Generative Model-Driven Synthetic Training Image Generation: An Approach
  to Cognition in Rail Defect Detection
Rahatara Ferdousi Chunsheng Yang M. Anwar Hossain Fedwa Laamarti M. Shamim Hossain Abdulmotaleb El Saddik
Recent advancements in cognitive computing, with the integration of deep
learning techniques, have facilitated the development of intelligent cognitive
systems (ICS). This is particularly beneficial in the context of rail defect
detection, where the ICS would emulate human-like analysis of image data for
defect patterns. Despite the success of Convolutional Neural Networks (CNN) in
visual defect classification, the scarcity of large datasets for rail defect
detection remains a challenge due to infrequent accident events that would
result in defective parts and images. Contemporary researchers have addressed
this data scarcity challenge by exploring rule-based and generative data
augmentation models. Among these, Variational Autoencoder (VAE) models can
generate realistic data without extensive baseline datasets for noise modeling.
This study proposes a VAE-based synthetic image generation technique for rail
defects, incorporating weight decay regularization and image reconstruction
loss to prevent overfitting. The proposed method is applied to create a
synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real
samples across five classes. Remarkably, 500 synthetic samples are generated
with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model
underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy
rates (98%-99%) in classifying the five defect classes. This research offers a
promising solution to the data scarcity challenge in rail defect detection,
showcasing the potential for robust ICS development in this domain.