Design of MPCANet fire image recognition model for deep learning

In view of the complicated background of the fire image, the complicated process of extracting the artificial feature, the poor generalization ability of the fire image, the low accuracy, false alarm rate, missing rate, the novel method for detecting fire images of multilinear principal component analysis (MPCA) was presented in the paper. The fire image recognition model was established by using MPCANet. Through the MPCA algorithm, the learning filter was used as the convolution kernel of deep learning network convolution layer, and the feature extraction of high dimensional images of tensor objects was taken, and candle images and fireworks images were taken as interference. Compared with other fire image recognition methods, the recognition accuracy of the proposed image recognition method reaches 97.5%, false alarm rate of 1.5%, missing rate of 1%. Experiments results show that this method could effectively solve the problems of fire image recognition.