深度学习的MPCANet火灾图像识别模型设计

针对火灾发生时，火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题，提出了张量对象特征提取的多线性主成分分析（Multilinear Principal Component Analysis，MPCA）深度学习算法的火灾图像识别新方法。利用MPCANet建立火灾图像识别模型，通过MPCA算法学习滤波器作为深度学习网络卷积层卷积核，对张量对象的高维图像进行特征提取，并把蜡烛图像和烟花图像作为干扰。通过仿真实验并与其他火灾图像识别方法对比得到提出的图像识别方法识别精度达到了97.5%、误报率1.5%、漏报率1%。实验表明:该方法可以有效解决火灾图像识别存在的问题。

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.