基于改进YOLOv8的森林火灾探测技术研究

Research on forest fire detection technology based on improved YOLOv8

森林火灾探测是当前的一个重点研究方向,然而,真实的森林火灾场景中存在大量的负样本数据,严重影响目标探测的性能,同时边端侧部署需要更加轻量化的模型。针对这一问题,提出了一种改进的YOLOv8方法,该方法首先引入EfficientViT模块到骨干网络(Backbone),通过级联分组注意力模块,减少计算开销;然后,在头部网络(Head)中引入CBAM模块,对骨干网络提取的特征进行特征增强,同时抑制噪声和无关信息;最后针对数据集的低质量样本,引入Wise-IoU损失函数,增强数据集训练效果。实验结果表明,改进后的YOLOv8模型对森林火灾的检测精度达到79.5%,检测速度达到75 FPS,整个模型的参数量降低了5.7%,对森林火灾探测具有重要意义。

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 deployment requires more lightweight models.To address this issue,this article proposes an improved YOLOv8 method,which firstly introduces the EfficientViT module to the backbone network and reduces computational overhead by cascading group attention modules.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.