A Reusable AI-Enabled Defect Detection System for Railway Using
  Ensembled CNN
Rahatara Ferdousi Fedwa Laamarti Chunsheng Yang Abdulmotaleb El Saddik
Accurate Defect detection is crucial for ensuring the trustworthiness of
intelligent railway systems. Current approaches rely on single deep-learning
models, like CNNs, which employ a large amount of data to capture underlying
patterns. Training a new defect classifier with limited samples often leads to
overfitting and poor performance on unseen images. To address this, researchers
have advocated transfer learning and fine-tuning the pre-trained models.
However, using a single backbone network in transfer learning still may cause
bottleneck issues and inconsistent performance if it is not suitable for a
specific problem domain. To overcome these challenges, we propose a reusable
AI-enabled defect detection approach. By combining ensemble learning with
transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the
classification accuracy and achieved consistent performance at a certain phase
of training. Our empirical analysis demonstrates better and more consistent
performance compared to other state-of-the-art approaches. The consistency
substantiates the reusability of the defect detection system for newly evolved
defected rail parts. Therefore we anticipate these findings to benefit further
research and development of reusable AI-enabled solutions for railway systems.