LAUSR

Traditional fire smoke detection methods mostly rely on manual algorithm extraction and sensor detection; however, these methods are slow and expensive to achieve discrimination. We proposed an improved convolutional neural network (CNN) to achieve fast analysis. The improved CNN can be used to liberate manpower. The network does not require complicated manual feature extraction to identify forest fire smoke. First, to alleviate the computational pressure and speed up the discrimination efficiency, kernel principal component analysis was performed on the experimental data set. To improve the robustness of the CNN and to avoid overfitting, optimization strategies were applied in multi-convolution kernels and batch normalization to improve loss functions. The experimental analysis shows that the CNN proposed in this study can learn the feature information automatically for smoke images in the early stages of fire automatically with a high recognition rate. As a result, the improved CNN enriches the theory of smoke discrimination in the early stages of a forest fire.