International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Railway safety continues to be a critical challenge in modern transportation systems due to recurring issues such as track defects, wildlife interference, and fire-related incidents, which frequently lead to severe accidents. This paper presents an intelligent IoT-enabled autonomous robotic system designed to address these challenges through a multi-modal hazard detection approach. The proposed robot autonomously navigates railway tracks while capturing visual data for structural assessment using edge detection techniques and Hough transformation to identify cracks and defects. In parallel, the system monitors wildlife intrusion and fire hazards to prevent potential collisions and emergencies. Detected information is transmitted wirelessly via IoT infrastructure to control centers, enabling real-time monitoring and rapid response actions. Machine learning models, including YOLO for object recognition and MobileNet-SSD for wildlife detection, are utilized to enhance detection accuracy. Experimental results from field tests demonstrate reliable performance with a low false alarm rate and high detection accuracy, while maintaining a cost-effective implementation of approximately INR 5,000 per unit. By reducing dependence on manual inspections and enabling continuous surveillance, the proposed system significantly enhances railway safety and operational efficiency.

Keywords: Railway safety, Autonomous robot, IoT-based monitoring, Computer vision

Downloads: | DOI: 10.17148/IJARCCE.2025.1412150