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

Sidi Mahmoudi, Maxime Gloesener, Mohamed Benkedadra, Jean-Sébastien Lerat

2025

Abstract

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×).

Paper Citation

in Harvard Style

Mahmoudi S., Gloesener M., Benkedadra M. and Lerat J. (2025). Edge AI System for Real-Time and Explainable Forest Fire Detection Using Compressed Deep Learning Models. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 847-854. DOI: 10.5220/0013382500003912

in Bibtex Style

@conference{visapp25,
author={Sidi Mahmoudi and Maxime Gloesener and Mohamed Benkedadra and Jean-Sébastien Lerat},
title={Edge AI System for Real-Time and Explainable Forest Fire Detection Using Compressed Deep Learning Models},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={847-854},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013382500003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Edge AI System for Real-Time and Explainable Forest Fire Detection Using Compressed Deep Learning Models
SN - 978-989-758-728-3
AU - Mahmoudi S.
AU - Gloesener M.
AU - Benkedadra M.
AU - Lerat J.
PY - 2025
SP - 847
EP - 854
DO - 10.5220/0013382500003912
PB - SciTePress