改进YOLOv7的无明火森林烟雾检测算法

Forest Smoke Detection MethodWithout Open Flames Based on Improved YOLOv7

快速准确判断森林火情对森林防火有着重要意义。然而,现有的森林烟雾检测模型提取的烟雾特征较为单一。因此,现有模型在图像中只有烟雾未见明火时的火情检测任务中表现并不理想。针对该问题,提出一种基于改进YOLOv7的无明火森林烟雾检测算法。该算法在主干网络中引入注意力机制CA和全卷积掩码自编码器框架FCMAE,使模型在提取语义特征的同时可以获取更为丰富的局部信息,解决了现有模型存在的特征崩溃问题。同时在预测网络中引入集中式特征金字塔CFP以加强特征的层内调节能力。另外,模型使用动态非单调调频的损失函数Wise-IoU以加强对低质量烟雾样本的检测能力。实验结果表明,相较于其他模型,该模型在检测无明火烟雾时表现上佳,精确率达到98.1%且mAP@50%达到99.1%。

Rapid and accurate judgment of forest fire is of great significance to forest fire prevention.However,the existing forest smoke detection model extracts a single smoke feature.Therefore,the existing models do not perform well in the fire detection task when there is only smoke in the image with no visible fire.To address this problem,an improved YOLOv7-based smoke detection algorithm for forests without open fires is proposed.The algorithm introduces the atten-tion mechanism CA and the full convolutional mask self-encoder framework FCMAE in the backbone network,so that the model can obtain richer local information while extracting semantic features and solves the feature collapse problem existing in the existing model.Meanwhile,a centralized feature pyramid CFP is introduced into the prediction network to strengthen the intra-layer adjustment ability of features.In addition,the model uses the loss function Wise-IoU with dynamic non-monotonic FM to strengthen the detection ability of low-quality smoke samples.The experimental results show that compared to other models,this model performs better in detecting smoke without open flames,with an accuracy of 98.1%,mAP@50% reaching 99.1%.