Enhanced Fire and Smoke Detection with YOLOv8: A Significant Performance Boost

Wildfires pose a significant threat, necessitating robust fire detection systems. Deep learning-based techniques, particularly those that utilize object detection models like YOLOv8, offer a promising alternative for fire and smoke detection. In this paper, we present a YOLOv8-based method for fire and smoke detection in images. We constructed a comprehensive dataset and trained a YOLOv8 model to achieve high accuracy in detecting fire and smoke. The results suggest the effectiveness of one of the recent models of YOLO.

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Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/issatk62463.2024.10808321
Primary Topic
Fire Detection and Safety Systems
Type
article
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article

Enhanced Fire and Smoke Detection with YOLOv8: A Significant Performance Boost

Mounir Sayadi, Moez Bouchouicha, Mohamed Ali Cherni, Imen Arfaoui et al.
Fire Detection and Safety Systems
article

Enhanced Fire and Smoke Detection with YOLOv8: A Significant Performance Boost

Mounir Sayadi, Moez Bouchouicha, Mohamed Ali Cherni, Imen Arfaoui, Hedi Jabnouni
article en

Abstract

Wildfires pose a significant threat, necessitating robust fire detection systems. Deep learning-based techniques, particularly those that utilize object detection models like YOLOv8, offer a promising alternative for fire and smoke detection. In this paper, we present a YOLOv8-based method for fire and smoke detection in images. We constructed a comprehensive dataset and trained a YOLOv8 model to achieve high accuracy in detecting fire and smoke. The results suggest the effectiveness of one of the recent models of YOLO.

Tunis University (TN), Centre National de la Recherche Scientifique (FR), Université de Toulon (FR), Laboratoire d’Informatique et Systèmes (FR), University of Sousse (TN)
Affordable and clean energy
Openalex Percentile: Top 70%
Fire Detection and Safety Systems
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Enhanced Fire and Smoke Detection with YOLOv8: A Significant Performance Boost — Mounir Sayadi, Moez Bouchouicha, et al. · (2024) | TGRS Research Map | TGRS