A Railway Track Crack and Hazard Detection System

Abstract

ABSTRACT

Railway transportation remains one of the most essential modes for passenger and freight movement worldwide. However, accidents caused by track faults, obstacles, and human or animal interference continue to pose serious safety concerns. Traditional inspection and monitoring methods are predominantly manual, resulting in delayed fault detection and limited accuracy. This paper proposes an automated and intelligent railway monitoring system that integrates real-time data acquisition, processing, and alert mechanisms to enhance operational safety. The system utilizes IoT-based communication and embedded control technologies to enable early detection of track anomalies and obstacles, thereby minimizing the risk of collisions and service interruptions. Simulation and experimental results demonstrate effective performance, rapid response, and scalability of the proposed model. Future development aims to incorporate AI-driven predictive maintenance for advanced fault detection and autonomous decision-making. The proposed approach offers a reliable and cost-efficient solution for improving safety and efficiency in modern railway networks.