A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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.
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.
Enhanced Fire and Smoke Detection with YOLOv8: A Significant Performance Boost
Jabnouni, Hedi (author) / Arfaoui, Imen (author) / Cherni, Mohamed Ali (author) / Sayadi, Mounir (author) / Bouchouicha, Moez (author)
2024-11-02
2159587 byte
Conference paper
Electronic Resource
English
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