AI-Driven Multi-Model Railway Track Surveillance and Intelligent Hazard Recognition System for Real-Time Safety Monitoring

Abstract

The rapid growth of railway transportation demands advanced monitoring systems to ensure passenger safety and uninterrupted train operations. This project presents an AI-Driven MultiModel Railway Track Surveillance and Intelligent Hazard Recognition System that utilizes multiple deep learning models for accurate and real-time detection of railway track defects and hazardous conditions. The proposed system integrates advanced computer vision techniques using models such as YOLOv8, EfficientNet, and ResNet to detect rail cracks, broken tracks, loose fasteners, obstacles, trespassers, vegetation intrusion, and track deformation from images and live video captured by track-mounted or drone-based cameras. The detected hazards are classified and their locations are recorded using GPS for precise identification. The processed information is transmitted to the railway control center through IoT communication, enabling instant alerts and rapid maintenance actions. A cloud-based dashboard allows continuous remote monitoring and analysis of railway infrastructure. The proposed multi-model architecture improves detection accuracy, minimizes false alarms, enhances operational reliability, and supports predictive maintenance. Overall, the system provides an intelligent, scalable, and cost-effective solution for improving railway safety, reducing accident risks, and enabling smart railway infrastructure management.