cs.CVcs.CRcs.LG
Smart Railway Obstruction Detection System using IoT and Computer Vision
May 7, 2026
The paper proposes NETRA, a cost-effective, internet-independent system using probabilistic sensor fusion and edge-AI classification on Raspberry Pi platforms to achieve high-accuracy, real-time detection of railway obstructions and wildlife.
Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. The December 2025 collision in Assam, in which seven elephants were killed by the Rajdhani Express, underscores the urgency of effective real-time detection. Existing solutions such as the optical fiber-based Gajraj system suffer from prohibitive costs (\$1000/km) and high false alarm rates, limiting deployment to only 20 of India's 101 elephant corridors. This paper proposes NETRA, a cost-effective, internet-independent intrusion detection system deployed on Raspberry Pi Zero W and Raspberry Pi 4 edge platforms. NETRA employs probabilistic sensor fusion integrating a PIR motion sensor and an HC-SR04 ultrasonic distance sensor with a tunable threshold (tau_c = 0.65), enabling event-driven camera activation that reduces unnecessary visual processing by 52%. Upon confirmed intrusion, edge-AI classification using MobileNet-SSD (Pi Zero) or YOLOv5 ONNX (Pi 4) identifies threats including humans, large animals, and track obstructions. Confirmed threats are transmitted via LoRa (868 MHz) to alert the locomotive driver within 2.4 seconds end-to-end. Experimental evaluation across 113 motion events demonstrated 95% detection accuracy with zero false alarms through probabilistic fusion, compared to 85% for binary methods. Raspberry Pi 4 with YOLOv5 achieved 83.5% elephant F1-score, a 5.6x improvement over Pi Zero's heuristic approach (14.8%). LoRa communication achieved 100% packet delivery across 1-2 km in field trials. NETRA reduces deployment cost by 75% (\$247/km vs \$1000/km for Gajraj) while providing unified detection of both wildlife and obstruction threats.
In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and An…
This paper proposes and evaluates a federated deep learning framework using auto…
MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library
MA-IDS proposes a Multi-Agent RAG framework that uses LLMs and a self-building E…
Optimizing IoT Intrusion Detection with Tabular Foundation Models for Smart City Forensics
The paper demonstrates that using the transformer-based foundation model TabPFNv…
Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modelling
This paper proposes an explainable threat attribution system for IoT networks th…
Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in…
This paper evaluates a novel black-box adversarial attack to demonstrate the vul…
Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models
This paper introduces an attribution-driven analysis of encoder-based Large Lang…
Structural Limits of Soft Fusion in Multi-Warden Covert Communication
The paper demonstrates that soft fusion in multi-warden covert communication has…
Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environ…
A hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attentio…