Smart Railway Obstruction Detection System using IoT and Computer Vision
This study addresses the critical safety challenge posed by railway track intrusions—such as wildlife or human-made obstacles—for which existing detection systems suffer from high costs and excessive false alarms, hindering large-scale deployment. To overcome these limitations, this work proposes NETRA, a low-cost, off-grid edge intelligence system that introduces a novel probabilistic fusion mechanism with adjustable thresholds to coordinate passive infrared (PIR) and ultrasonic sensors in triggering the camera, thereby reducing false positives and cutting unnecessary image processing by 52%. By integrating lightweight MobileNet-SSD and YOLOv5 ONNX models, NETRA achieves unified object detection on a Raspberry Pi. Evaluated over 113 intrusion events, the system attains 95% detection accuracy with zero false alarms, an F1-score of 83.5% for elephant identification, 100% alert delivery via LoRa (868 MHz) within 1–2 km, an end-to-end latency of only 2.4 seconds, and a 75% reduction in deployment cost.