Edge AI System for Real-Time and Explainable Forest Fire Detection Using Compressed Deep Learning Models

Forests are vital natural resources but are highly vulnerable to disasters, both natural (e.g., lightining strikes) and human induced. Early and automated detection of forest fire and smoke is critical for mitigating damages. The main challenge of this kind of application is to provide accurate, explainable, real-time and lightweight solutions that can be easily deployable by and for users like firefighters. This paper presents an embedded and explainable artificial intelligence “Edge AI” system, for real-time forest fire, and smoke detection, using compressed Deep Learning (DL) models. Our model compression approach allowed to provide lightweight models for Edge AI deployment. Experimental evaluation on a preprocessed dataset composed of 1500 images demonstrated a test accuracy of 98% with a lightweight model running in real-time on a Jetson Xavier Edge AI resource. The compression methods preserved the same accuracy, while accelerating computation (3× to 18× speedup), reducing memory consumption ( 3.8× to 10.6×), and reducing energy consumption (3.5× to 6.3×).