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

TL;DR

This paper presents an FPGA-based edge AI system for railway fault detection that uses CNNs for real-time, automated inspection, achieving high accuracy and superior energy efficiency compared to GPU and CPU systems.

Contribution

The study introduces a novel FPGA-based railway inspection system that enhances automation, detection accuracy, and energy efficiency over traditional methods.

Findings

Detection accuracy of 88.9% achieved.

System is 1.39 times more energy-efficient than GPU implementations.

System is 4.67 times more energy-efficient than CPU implementations.

Abstract

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.