An Edge AI System Based on FPGA Platform for Railway Fault Detection
This addresses automation and efficiency needs for railway safety, though it is incremental as it applies existing CNN methods to a new domain with hardware optimization.
The study tackled railway fault detection by introducing an FPGA-based edge AI system that uses CNNs on track images, achieving 88.9% detection accuracy and improving energy efficiency by 1.39x over GPU and 4.67x over CPU.
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.