一种多网络模型融合的烟雾检测方法

为降低云雾等类烟雾目标引起的烟雾检测虚警现象，提出一种多网络模型融合的烟雾检测方法。在采用VGG16网络提取烟雾细节特征的基础上，与ResNet50网络特征提取层进行融合，提取到更多细微特征，采用跳跃连接机制将图像信息传递到神经网络的更深层，避免烟雾图像重要特征的丢失，并解决因梯度消失导致的欠拟合问题。训练过程采用基于同构空间下的特征迁移学习方法，解决小样本训练难题，在新的目标检测领域进行重新训练，更有利于将网络模型融合，重新搭建全连接层输出检测结构，采用随机失活的方法，提高模型泛化能力。实验结果表明，与目前流行的深度卷积网络相比，该方法虚警率低，准确率和召回率高。

A smoke detection method based on fusing multiple network models

In order to reduce the false alarm phenomenon of smoke detection caused by cloud and fog, a smoke detection method based on fusing multiple network models is proposed. On the basis of using VGG16 network to extract the detailed features of smoke, it is fused with the ResNet50 network feature extraction layer to extract more subtle features. The skip connection mechanism is used to transfer the image information to the deeper layer of the neural network, in order to avoid the loss of important features of smoke image and solve the under-fitting problem caused by the gradient disappearance.The training process adopts the feature transfer learning method based on isomorphic space to solve the small sample training problem, retrain in the new target detection field, better integrate the network model, rebuild the output detection structure of the whole connection layer, and adopt the random inactivation method to improve the generalization ability of the model.Experimental results show that, compared with the current popular deep convolutional network, this method has lower false alarm rate and higher accuracy and recall rate.