YOLOv6 for Fire Images detection

• Published in: Proceedings (International Conference on Cyberworlds. Online) pp. 500 - 501
• Main Authors: Jabnouni, Hedi, Arfaoui, Imen, Cherni, Mohamed Ali, Bouchouicha, Moez, Sayadi, Mounir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.10.2023
• Subjects: Computer vision; Deep learning; Fire detection; Forest Fire detection; YOLOv6
• ISSN: 2642-3596
• DOI: 10.1109/CW58918.2023.00086

Early fire forest detection is crucial for fast and effective intervention. Many research have been done on this subject starting by sensor based systems and arriving to image processing which leverage the computer vision advancements. Our work refers to one of the latest algorithms in forest fire detection: YOLO. We present in this paper a detailed description of the architecture of the YOLO algorithm with an emphasis to the YOLOv6 which is the latest version of the YOLO algorithms. The performance of the studied algorithm is evaluated on a personal database containing 28334 images, with 10534 forest fire images and 17800 non-fire images. The experimental results of applying the YOLOv6 proved the efficiency of the method in fast and accurate fires detection even in large size images and low resolutions. This result makes the studied algorithm so suitable for both satellite and ground based images analysis.