Small Target Forest Fire Recognition Method based on Deep Learning

Due to the small size of the fire target in the early stage of the forest fire, the model has the problem of missing detection of the small target forest fire. This paper proposes a forest fire recognition method based on YOLOv5 small target detection. In order to obtain flame space information at different scales and obtain an output containing abstract feature information, replace the SPP module in the YOLOv5 backbone network with the WASP module; in order to enhance the expression of flame features, add the Triplet attention module to the C3 module; In order to improve the detection effect of the detection algorithm on small flames, a small target detection layer is added. In this paper, a set of ablation experiments and a set of comparative experiments are designed to verify the effectiveness of the proposed algorithm. The experimental results show that compared with the original YOLOv5 algorithm, the algorithm in this paper has improved mAP@50 by 3.49%, and can accurately detect small target forest fire targets, effectively improving the ability to prevent forest fires.