A Fire Detection Model Based on Tiny-YOLOv3 with Hyperparameters Improvement

Fires are the most devastating disasters that the world can face. Thereby, it is crucial to exactly identify fire areas in video surveillance scenes, to overcome the shortcomings of the existing fire detection methods. Recently, deep learning models have been widely used for fire recognition applications. Indeed, a novel deep fire detection method is introduced in this paper. An improved fire model based on tiny-YOLOv3 (You Only Look Once version 3) network is developed in order to enhance the detection accuracy. The main idea is the tiny-YOLOv3 improvement according to the refined proposed training hyperparameters. The generated model is trained and evaluated on the constructed and manually labeled dataset. Results show that applying the proposed training heuristics with the tiny-YOLOv3 network improves the fire detection performance with 81.65% of mean Average Precision (mAP). Also, the designed model outperforms the related works with a detection precision of 97.6%.