Railguard: An IOT Powered Smart Railway Safety and Monitoring System

The rising number of railway accidents caused by human drowsiness, track failures, and unforeseen barriers reflects the imperative need for an automated and smart safety system. This paper proposes an IoT-based Smart Railway Safety System to keep track of real-time conditions and provide secure train operations by integrating four major modules: Drowsiness Detection, Obstacle Detection, Track Fault Detection, and Alert & Notification System. Drowsiness Detection module uses computer vision and Eye Aspect Ratio (EAR) evaluation to determine the alertness of the loco pilot by measuring eye closure time through a live camera feed. Extended eye closure initiates instant alerts to avoid probable accidents. The Obstacle Detection system using an ultrasonic sensor constantly looks for unforeseen objects in the front region and stops the train when required. Track Fault Detection utilizes an infrared (IR) sensor to detect cracks or gaps on the railway track those mimic breakages, making early intervention possible. The Alert & Notification module encompasses a GSM and GPS system to send real-time SMS messages with geographic co-ordinates to concerned authorities, and an on-board buzzer alerts the driver. Performance testing illustrates the high reliability of the system, with response rates between 98.3% and 99.1% and response times of less than 150 milliseconds. Compared with conventional manually monitored systems, this solution presents an economical, proactive, and real-time method for railway safety. The combination of hardware-based sensing and software-based intelligence makes this system a promising model for contemporary railway accident avoidance and safety promotion.