Forest fire detection is a key research direction at present.However,there are a large number of negative sample data in real forest fire scenarios,which seriously affects the performance of target detection.At the same time,edge to edge deploy-ment requires more lightweight models.To address this issue,this article proposes an improved YOLOv8 method,which firstly in-troduces the EfficientViT module to the backbone network and reduces computational overhead by cascading group attention mod-ules.Then,the CBAM module is introduced into the head network to enhance the features extracted by the backbone network,while suppressing noise and irrelevant information.Finally,for low-quality samples in the dataset,the Wise-IoU loss function is introduced to enhance the training effect of the dataset.The experimental results show that the improved YOLOv8 model achieves a detection accuracy of 79.5%for forest fires,a detection speed of 75 FPS,and a 5.7%reduction in the parameter count of the entire model,which is of great significance for forest fire detection.
关键词
YOLOv8/森林火灾探测/图像分析/EfficientViT/注意力机制
Key words
YOLOv8/forest fire detection/image analysis/EfficientViT/attention mechanism
维普
万方数据