Automatic Real Time Multi Hazard Detection and Prevention System for Railway Safety using Machine Learning and IoT

Abstract

Railway transportation is one of  the  most essential modes of travel and goods movement, but it is highly vulnerable to accidents caused by track cracks obstacles, fires, and unauthorized access. This paper proposes an intelligent real-time multi-hazard detection and prevention system that combines the Internet of Things (IoT) with Machine Learning (ML) to enhance railway safety. The system employs multiple sensors and a camera module to monitor the track and surrounding environment continuously. Detected hazards trigger immediate alerts through wireless communication modules, enabling timely preventive action. Machine learning algorithms are utilized to improve detection accuracy and reduce false alarms. The proposed model operates autonomously, minimizing human intervention while ensuring faster response times. Experimental results confirm that the system is cost-effective, scalable, and capable of significantly improving railway safety and reliability.