ForestProtector: An IoT Architecture Integrating Machine Vision and Deep Reinforcement Learning for Efficient Wildfire Monitoring

TL;DR

This paper introduces ForestProtector, an IoT-based wildfire monitoring system that combines machine vision and deep reinforcement learning to efficiently detect fires over large forest areas with minimal human intervention.

Contribution

It presents a novel low-cost IoT architecture integrating machine vision and deep reinforcement learning for dynamic wildfire surveillance.

Findings

Effective long-distance smoke detection using computer vision.

Dynamic camera control improves monitoring coverage.

Reduced false positives in wildfire detection.

Abstract

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.