FireSpot-Seg: A Segmentation Database for Early-Stage Wildfire Smoke Detection with Using YOLOv8-Seg

Wildfires are among the most destructive natural disasters, causing extensive damage to ecosystems, communities, and economies. In recent years, computer vision techniques have emerged to improve detection accuracy and efficiency. One of the interesting approaches is the segmentation method, which is particularly suitable for this task due to the complex behavior of wildfire smoke, which varies in volume, density, and color based on factors like wind speed, humidity, and temperature. However, there is a scarcity of segmentation datasets for wildfire smoke. To address this gap, we created a dataset in YOLO segmentation format and binary mask format containing 2,913 images of wildfire smoke. This paper presents a new wildfire smoke detection segmentation dataset derived from the FireSpot dataset. Our implementation of the YOLOv8s-Seg model used as a baseline for future development, achieved 94.4 % accuracy in bounding box detection and 92.8 % in segmentation detection at a 0.3 IoU threshold. These results demonstrate the model's performance and present the use case of this dataset for researchers developing new segmentation models.