Forest Fire Detection based on YOLOv8

Forest fires are one of the most destructive natural disasters, causing significant ecological, economic, and social losses. Early detection and rapid response are critical for minimizing the damage caused by these fires. Traditional forest fire detection methods, such as manual monitoring and satellite imagery, often suffer from delays and inaccuracies. With the rapid advancement of computer vision and deep learning technologies, automated fire detection systems have emerged as a promising solution. This paper presents a novel forest fire detection system based on the latest object detection algorithm, which aims to provide real-time, accurate, and efficient fire detection capabilities. The proposed system leverages the YOLOv8 algorithm, which is renowned for its high detection speed and accuracy. The system processes real-time video feeds from surveillance cameras installed in forested areas to detect flames, smoke, and other fire-related indicators. We trained the YOLOv8 model using a comprehensive dataset containing annotated images of forest fires and non-fire scenes. The model was optimized for detecting small and partially obscured fires, which are common in early-stage forest fire scenarios.