Fault Detection and Semantic Segmentation on Railway Track Using Deep Fusion Model

Abstract

Track quality and train operating safety depend on routine inspections of track components. According to statistics from the Federal Railroad Administration (FRA), train accidents in the United States are primarily caused by the failure of track components. Existing technologies, such as fault detection and fault segmentation, have been used to increase efficiency. However, these models sometimes fail when dealing with small or broken cracks. To address this issue, we suggest implementing a fusion model that uses a deep convolutional neural network for fault detection and semantic segmentation on railway lines. The ResNet50 network is used for fault identification in this model, while the Deep Residual U-Net network is applied for semantic segmentation. The identified defects are isolated, and decisions can be made from resulting binary image segments on whether to inspect or disregard detected faults. These defects include damages observed on railway tracks and any absent or damaged components such as spikes, clips, and rails. Comprehensive tests carried out on a track dataset obtained from the Kaggle repository demonstrate that the suggested approach is capable of accomplishing the following: 1) surpassing current leading models by achieving 94.3% mean average precision (mAP) for fault detection and 79 frames/sec, and 2) achieving 95.6% accuracy in semantic segmentation. Therefore, this fusion model helps inspect tracks by detecting and segmenting faults, thereby reducing derailment cases and other railroad-related accidents.

Keywords

• Deep fusion
• deep learning
• fault detection
• railroad safety
• semantic segmentation