SMART RAILWAY TRACK OBSTACLE MONITORING SYSTEM WITH INTENT-BASED RISK CLASSIFICATION

Abstract

Railway transportation is highly vulnerable to accidents caused by unexpected obstacles such as fallen trees, vehicles, animals, debris, landslides, or intentional sabotage. To address these risks, this paper proposes a Smart Railway Track Obstacle Monitoring System with Intent-Based Risk Classification that detects and analyses track obstructions in real time. The system integrates cameras, infrared sensors, vibration sensors, and IoT-enabled devices deployed along railway tracks. Using Artificial Intelligence and deep learning algorithms, it not only identifies obstacles but also classifies them based on intent—accidental, natural, or malicious—and assigns a risk level (low, medium, or high). Edge computing ensures immediate processing, while a centralized dashboard enables railway authorities to receive alerts and take rapid action, including speed control or emergency responses. By combining IoT, AI, and real-time analytics, the proposed system reduces false alarms, improves response time, and enhances railway safety and operational efficiency.