# A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN

arXiv: https://arxiv.org/abs/2311.14824 · HTML: https://mlsanity.com/paper/2311.14824/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)

## Built on by (most cited first, 2 of 2)

- [DefectTwin: When LLM Meets Digital Twin for Railway Defect Inspection](https://mlsanity.com/paper/2409.06725/) (2024)
- [Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey](https://mlsanity.com/paper/2610.00363/) (2026)

## Abstract (arXiv)

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.