# An Edge AI System Based on FPGA Platform for Railway Fault Detection

arXiv: https://arxiv.org/abs/2408.15245 · HTML: https://mlsanity.com/paper/2408.15245/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 8 Aug 2024.

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

- [PD-Quant: Post-Training Quantization based on Prediction Difference Metric](https://mlsanity.com/paper/2212.07048/) (2022)
- [RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization](https://mlsanity.com/paper/2204.12322/) (2022)
- [Joint Source-Channel Coding System for 6G Communication: Design, Prototype and Future Directions](https://mlsanity.com/paper/2310.01024/) (2023)

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

- [Beyond the GPU: The Strategic Role of FPGAs in the Next Wave of AI](https://mlsanity.com/paper/2511.11614/) (2025)
- [Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey](https://mlsanity.com/paper/2610.00363/) (2026)

## Abstract (arXiv)

As the demands for railway transportation safety increase, traditional methods of rail track inspection no longer meet the needs of modern railway systems. To address the issues of automation and efficiency in rail fault detection, this study introduces a railway inspection system based on Field Programmable Gate Array (FPGA). This edge AI system collects track images via cameras and uses Convolutional Neural Networks (CNN) to perform real-time detection of track defects and automatically reports fault information. The innovation of this system lies in its high level of automation and detection efficiency. The neural network approach employed by this system achieves a detection accuracy of 88.9%, significantly enhancing the reliability and efficiency of detection. Experimental results demonstrate that this FPGA-based system is 1.39* and 4.67* better in energy efficiency than peer implementation on the GPU and CPU platform, respectively.