Forest Flame Detection Using Yolo v8 - A Deep Learning Approach

Forest fires, often referred to as “Wild Fires,” are the most frequent natural disasters affecting forest ecosystems worldwide. They have a profound and destructive impact on the technological solutions have been proposed and deep learning stands out as a promising approach. Most of the previous papers worked on this issue using different models like CNN, YOLOV3 etc. which are having various set of limitations like flame frequency, insufficient training data etc. This paper proposes the forest flame detection techniques using the YOLOV8 model. By applying a single-shot object detection algorithm, objects can be identified and categorized by simultaneously analyzing the entire image. YOLOv8 is a powerful tool for building flame detection systems using custom datasets. This approach grants a considerable speed advantage over object detection methods reliant on region proposal techniques. The proposed method outperforms traditional approaches, enhancing the effectiveness of flame detection systems and supporting firefighting efforts. This model works on the improvement of advanced flame detection systems and support firefighting efforts for the preservation of forests and protection of lives.