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{\deg} 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.

翻译：森林火灾的早期探测对于最小化其造成的环境与社会经济损失至关重要。事实上，火灾持续时间与扑救难度及成本直接相关。例如，燃烧1分钟的火灾可能仅需1升水即可扑灭，而燃烧2分钟的火灾可能需要100升水，燃烧10分钟的火灾则可能需要1000升水。另一方面，基于新兴技术（如遥感、PTZ摄像机、无人机）的现有火灾探测系统往往成本高昂且需要人工干预，使得对大范围区域进行持续监测变得不切实际。为应对这一挑战，本研究提出了一种低成本的森林火灾探测系统。该系统利用具备计算机视觉能力的中央网关设备，对远距离360°视野范围内的烟雾进行监测。一个深度强化学习智能体通过动态控制摄像机朝向，并利用来自分布式物联网设备的实时传感器数据（烟雾浓度、环境温度与湿度），从而增强监测能力。该方法实现了对广阔区域的自动化野火监测，同时降低了误报率。