Two-phase flow pattern identification method based on multi-scale convolutional network

The invention belongs to the technical field of image processing and deep learning, and relates to efficient image classification processing, in particular to a two-phase flow pattern identification method based on a multi-scale convolutional network. The method characterized by at least comprising ten steps. According to the method, an RBF neural network is used for image reconstruction, an image data set of the convolutional neural network is constructed, the data set is divided into a training set, a verification set and a test set according to the proportion of 4: 1: 1, and the test set can be used for network performance testing after multi-scale convolution class network training is completed. Accurate identification of a core type flow pattern and a ring type flow pattern is realized. 本发明属于图像处理与深度学习技术领域，涉及高效的图像分类处理，尤其涉及一种基于多尺度卷积网络的两相流流型识别方法,其特征是：至少包括十个步骤。本文利用RBF神经网络进行图像重建，构建卷积神经网络的图像数据集，并将数据集按4：1：1的比例划分为训练集、验证集和测试集，多尺度卷积分类网络训练完成后可以使用测试集来进行网络性能的测试。它实现芯型流型和环型流型的准确识别。