Improvement of flow pattern identification technology for ECT system based on DCGAN dataset enhancement

The flow pattern recognition technology based on capacitive tomography (ECT) system is widely used in dynamic measurement of various industrial processes due to its non-invasive and visual advantages. To improve the flow pattern identification accuracy of ECT system, recognition algorithm based on neural network model is widely used. But the ECT system can’t get a lot of ECT system measured samples, resulting in low accuracy, low model generalization. To solve this problem, this paper adopted the dataset amplification method based on deep convolution generative adversarial network (DCGAN), and used the images reconstructed by the preprocessed LSSVM method to build a flow pattern image generation model based on DCGAN for experiments. Gan-train and GAN-test were used to evaluate the generated images. After that, the amplified dataset is used for flow pattern identification based on YOLOv3. The experimental results show that the DCGAN method can effectively amplify the original data set and generate images with high accuracy and richness. Together with the target detection algorithm of YOLOv3, the flow pattern recognition accuracy of ECT system can be improved to a great extent.