用低成本边缘设备实现铁路侵入实时检测，兼顾野生动物与人为障碍物
Smart Railway Obstruction Detection System using IoT and Computer Vision
- 基于树莓派和多传感器融合，按需触发摄像头降低52%无效计算
- 检测准确率95%且零误报，大象识别F1-score达83.5%（提升5.6倍）
- 适合铁路安全系统升级，尤其适用于经费有限的偏远线路
铁路轨道侵入是印度铁路面临的关键安全挑战，涵盖野生动物闯入和人为恶意阻塞。2025年12月阿萨姆邦列车撞死七头大象事件凸显了实时检测的紧迫性。现有基于光纤的Gajraj系统成本高达每公里1000美元，且误报率高，仅部署于101条大象走廊中的20条。本文提出NETRA系统，采用树莓派Zero W和4型作为边缘平台，无需互联网即可运行。通过集成红外运动传感器与超声波测距传感器，并设置可调阈值（tau_c = 0.65），实现概率融合，使事件驱动的摄像头激活减少52%的冗余视觉处理。确认入侵后，使用MobileNet-SSD（Pi Zero）或YOLOv5 ONNX（Pi 4）进行边缘端威胁分类，识别人类、大型动物及轨道障碍物。确认威胁通过LoRa（868 MHz）传输至机车司机，端到端延迟仅2.4秒。在113次运动事件的实验中，概率融合方法达到95%检测准确率且零误报，优于二元方法的85%。树莓派4搭配YOLOv5对大象识别的F1-score为83.5%，比树莓派零的启发式方法（14.8%）提升5.6倍。现场测试中，LoRa在1-2公里范围内实现100%包送达率。NETRA将部署成本降低75%（每公里247美元，相较Gajraj的1000美元），同时统一应对野生动物与轨道障碍物威胁。
原文摘要 · Abstract (English)
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.
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