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.