基于轻量级卷积神经网络的烟雾识别算法

由于烟雾图像场景模糊不清，背景复杂多变，难以捕获到有效特征，导致算法识别误报率和漏报率较高；此外，深度卷积神经网络结构复杂，参数繁多，难以缩短其计算时间至1 ms内，这成为实时火灾预警的一大难题. 为了解决上述问题，提出了一种基于4种Inception结构的轻量级卷积神经网络SInception （sequeeze-and-excitation inception）在此基础上加入SE Block （sequeeze-and-excitation block）用于对烟雾特征进行重新分配；同时，为了避免由于训练样本不足引起的过拟合，原始数据集上采用数据增强技术以及生成对抗网络生成更多训练样本，并在后续实验中采用了融合暗通道先验特征的策略. 实验结果表明:该网络在增强的数据集GAN-Aug-YUAN上将识别误报率降为0的同时将准确率提升至99.65%，且计算时间减少到0.26 ms. 

Smoke Recognition Algorithm Based on Lightweight Convolutional Neural Network

As smoke images are ambiguous, and the background is complex and variable, it is difficult to capture the effective features, resulting in high false positive rates and false negative rates. In addition, the deep convolutional neural network has a complicated structure and many parameters, and it is difficult to control the calculation time within one millisecond, which becomes a major problem for real-time fire warning. In order to deal with these obstacles, a lightweight convolutional neural network SInception (sequeeze-and-excitation inception) is proposed on the basis of four Inception structures, which significantly reduces the number of network parameters and calculation amount. It adds SE Block (sequeeze-and-excitation block) for smoke so that features are redistributed to make them more representative of smoke images. In order to avoid over-fitting due to insufficient training samples, for the data enhancement techniques on the original dataset and generative adversarial network are used to generate more training samples. Subsequently a strategy of integrating the priori features of dark channels is used in experiments. Finally, the network raises the accuracy rate to 99.65%, while for the dataset GAN-Aug-YUAN it reduces the false alarm rate to 0, and the calculation time is only 0.26 ms.