嵌入通道注意力的YOLOv4火灾烟雾检测模型

Firesmoke detection model based on YOLOv4 with channel attention

为提高火灾烟雾检测模型的性能,以及避免繁琐的手工烟雾特征提取过程,本文提出一种基于卷积神经网络YOLOv4的火灾烟雾检测模型。该方法以CSPDarkNet53为主干网络,在主干网络的最后一层加入了13×13.9×9.5×5.1×1四个不同尺度的最大池化,多尺度特征融合中采用了PANet(Path Aggregation Network)以提高网络特征提取能力;为了增强网络预测头提取有效烟雾信息的能力,在网络预测头加入通道注意力网络。针对火灾烟雾数据集,候选框的尺寸使用K-means进行聚类以得到更加符合贴近火灾烟雾数据集的尺寸;由于本文仅识别火灾烟雾一种类别,所以精简损失函数,剔除分类误差,使算法收敛得更快。训练阶段使用了图像翻转、放缩和随机擦除等数据增强方法以降低过拟合的风险。实验结果表明,所提出的火灾烟雾检测模型精度高,其准确率达到92.5%,召回率达到87.7%,同时检测速度可达5.1帧/s,提高了火灾烟雾检测模型的性能。

To improve the precision and recall rate of fire smoke detection model in multi-scene fire smoke detection applications,and avoid the tedious manual smoke feature extraction process,a fire smoke detection model is proposed which is based on convolutional neural network YOLOv4.In the last layer of the backbone network,four different scales of maximum pooling are added:13×13,9×9,5×5,and 1×1.The multi-scale feature fusion uses PANet(Path Aggregation Network)to improve network feature extraction capabilities.In addition,a channel attention network is added to the network prediction head to enhance the ability of the YOLO Head to extract effective smoke information.For the fire smoke data set,the size of the candidate frame is clustered using the K-means algorithm to get a size closer to the fire smoke data set.Due to the identification of smoke,the loss function is simplified,the classification error is eliminated,and the algorithm converges faster.Data enhancement methods such as image flipping and random erasure are used in the training phase to reduce the risk of overfitting.Experimental results show that the fire smoke detection model has excellent performance.Its precision can reach 92.5%,Recall can reach 87.7%,and the detection speed can reach 5.1 frames/s,which improves the performance of fire smoke detection model in multi-scene fire smoke detection applications.