INTELLIGENT RAILWAY GATE AUTOMATION USING IOT AND ML

This research presents a novel solution to the safety hazards inherent in conventional manual railway crossings, which are highly susceptible to human error and environmental limitations such as heavy precipitation or fog.We propose an automated framework that fuses Internet of Things (IoT) connectivity with YOLOv8-based computer vision to modernize existing gatekeeping methods.The architecture utilizes longrange sensing to compute a dynamic Time to Arrival (TTA), ensuring gate closure occurs only when necessary to alleviate traffic bottlenecks.By implementing the YOLOv8 model, the system achieves intelligent hazard classification, successfully distinguishing between vehicles and pedestrians-a capability often lacking in standard binary infrared sensors.Our findings indicate that this autonomous approach significantly enhances operational safety and provides a dependable, cost-effective alternative for high-speed rail corridors and unmanned level crossings.