用低成本物联网系统实现森林火灾自动监测，靠视觉与强化学习减少误报。
ForestProtector: An IoT Architecture Integrating Machine Vision and Deep Reinforcement Learning for Efficient Wildfire Monitoring
- 通过视觉识别烟雾并动态控制摄像头方向，实现大范围自动巡检。
- 利用环境数据优化决策，使10分钟火情需水量达1000升时仍可及时响应。
- 适合需要长期、无人值守监控的偏远林区，降低人力与设备成本。
森林火灾的早期发现对减轻环境与社会经济损失至关重要。研究表明，燃烧时间越长，扑灭难度与用水量急剧上升：1分钟火情仅需1升水，2分钟增至100升，10分钟则需1000升。现有基于遥感、PTZ相机、无人机等技术的监测系统往往成本高昂且依赖人工，难以实现大范围连续监控。为此，本文提出一种低成本森林火灾检测系统，采用具备360°视野的中心网关设备，结合计算机视觉技术远距离探测烟雾。通过深度强化学习代理，实时整合分布式物联网设备提供的烟雾浓度、环境温度和湿度数据，动态调整摄像头视角，实现自动化、低误报率的大面积火灾监控。
原文摘要 · Abstract (English)
Early detection of forest fires is crucial to minimizing the environmental and socioeconomic damage they cause. Indeed, a fire's duration directly correlates with the difficulty and cost of extinguishing it. For instance, a fire burning for 1 minute might require 1 liter of water to extinguish, while a 2-minute fire could demand 100 liters, and a 10-minute fire might necessitate 1,000 liters. On the other hand, existing fire detection systems based on novel technologies (e.g., remote sensing, PTZ cameras, UAVs) are often expensive and require human intervention, making continuous monitoring of large areas impractical. To address this challenge, this work proposes a low-cost forest fire detection system that utilizes a central gateway device with computer vision capabilities to monitor a 360° field of view for smoke at long distances. A deep reinforcement learning agent enhances surveillance by dynamically controlling the camera's orientation, leveraging real-time sensor data (smoke levels, ambient temperature, and humidity) from distributed IoT devices. This approach enables automated wildfire monitoring across expansive areas while reducing false positives.
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