IOT Based Railway Track Fault Detection

Abstract: Railway track failures such as cracks, misalignment, and obstacles pose serious safety risks and often remain undetected due to the limitations of manual inspection methods. To address this issue, this paper presents an Internet of Things (IoT) based railway track fault detection system that enables continuous and real-time monitoring using intelligent video analysis. A Wi-Fi enabled camera mounted on a moving inspection unit captures live video of the railway track, which is processed using computer vision techniques and a YOLO-based deep learning model to identify structural defects. The detection results are transmitted to a cloud platform using Firebase, allowing remote monitoring and instant alert generation. An ESP32 microcontroller retrieves cloud commands to automatically control the movement of the inspection unit through a relay-driven motor mechanism, ensuring immediate stoppage upon fault detection. The proposed system minimizes human intervention, improves detection accuracy, and offers a cost-effective and scalable solution for enhancing railway safety and enabling predictive maintenance.

Keywords: Railway Track Fault Detection, Internet of Things (IoT), Computer Vision, YOLO, ESP32, Real-Time Monitoring, Cloud Computing.