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.

准确的缺陷检测对于确保智能铁路系统的可信性至关重要。为了解决样本有限时训练新的缺陷分类器导致过拟合和在未见图像上表现不佳的问题，我们提出了一种可重复使用的基于人工智能的缺陷检测方法。通过将集成学习与迁移学习模型（VGG-19、MobileNetV3 和 ResNet-50）相结合，我们提高了分类准确性，并在某一训练阶段实现了一致的性能。与其他最先进的方法相比，我们的实证分析表明了更好和更一致的性能。这种一致性证实了缺陷检测系统对于新出现的有缺陷的铁路部件的可重复使用性。因此，我们预计这些发现将有助于铁路系统可重复使用的基于人工智能的解决方案的进一步研究和发展。