湖北林业科技

At present, forest fire identification data sets mainly focus on flame identification, lack of smoke identification, and have limitations in the actual forest fire detection. In view of this limitation, this paper proposes an improved YOLOv8 method based on attention mechanism, which uses the pre-trained YOLOv8 model to conduct preliminary training on the smoke data set to improve its smoke recognition effect. The model is further used to label the flame data set, and the smoke flame data set containing the smoke and flame labeling information is obtained. Finally, the improved model proposed in this paper is used to retrain the model on the data set.The average accuracy of mAP0.5, mAP0.75, mAP0.5:0.95 and mAR0.5:0.95 on the smoke flame data set were 89.5%, 70.4%, 61.7% and 66.8%, respectively. The model was able to accurately identify smoke and flames in real-world forest fire images.The forest fire identification method proposed in this paper also includes smoke into the scope of forest fire early warning identification to improve the efficiency and accuracy of fire identification, and this technology can provide technical support for forest fire early warning.