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.
Authors
- Mounir Sayadi (ORCID: https://orcid.org/0000-0003-4270-421X)
- Moez Bouchouicha (ORCID: https://orcid.org/0000-0002-8174-6343)
- Mohamed Ali Cherni
- Imen Arfaoui
- Hedi Jabnouni
Institutions
- Tunis University (TN)
- Centre National de la Recherche Scientifique (FR)
- Université de Toulon (FR)
- Laboratoire d’Informatique et Systèmes (FR)
- University of Sousse (TN)
Publication Details
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/issatk62463.2024.10808321
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00