Enhancing Fire Detection with YOLO Models: A Bayesian Hyperparameter Tuning Approach

Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach improves the mean average precision at an Intersection over Union (IoU) threshold of 0.5 (mAP50) of the YOLOv8s, YOLOv10s, YOLOv8l, and YOLOv10l models by 0.26, 0.21, 0.84, and 0.63, respectively, compared to models trained with the default hyperparameters. The performance gains are more pronounced in larger models, YOLOv8l and YOLOv10l, than in their smaller counterparts, YOLOv8s and YOLOv10s. Furthermore, YOLOv8 models consistently outperform YOLOv10, with mAP50 improvements of 0.26 for YOLOv8s over YOLOv10s and 0.65 for YOLOv8l over YOLOv10l when trained with BT. These results establish YOLOv8 as the preferred model for fire detection applications where detection performance is prioritized.