## 4 Flow Pattern Recognition Results and Discussion
### Discussion  
Traditional flow pattern recognition is subjective, and it is difficult to form a recognized standard for flow pattern classification, and the recognition accuracy is not high. In order to conduct research on flow pattern recognition, more and more experts are now using machine learning approaches, such as neural networks, support vector machines (SVM), random forests, and so forth. The feature fusion recognition approach suggested in this paper was tested to ensure its efficacy. Alexnet, VGG16, VGG19, Resnet34, and Resnet50  
Figure 17: Training loss and test accuracy of the overall flow pattern of the fusion mode.  
Figure 18: Confusion matrix of flow type classification.  
classification models in convolutional neural networks are selected for the comparative study of stream type recognition. The data set was selected from the ECT image data that were collected and classified, and only the training learning of images was performed. The data division is shown in Table 5.  
The robustness and accuracy of the feature fusion flow pattern recognition model for two-phase flow pattern recognition were verified through comparative analysis, as shown in Figure 19. The recognition results of VGG16 and VGG19 demonstrate that the model's recognition accuracy really increases with network depth, which is a result of the analysis in Figure 19. The recognition results of Reesnet34 and Resnet50 are also similar. Comparing the model proposed in this paper with some typical classification algorithms, it is found that the feature fusion model has a high flow pattern identification accuracy, which has a significant advantage of 6-17% improvement compared with other single-channel models.  
The experimental findings demonstrate the effectiveness of the suggested flow pattern detection approach, and the overall accuracy of the two-phase flow pattern identification for the subsea jumper reaches 95.3%. However, the flow pattern information collected in this study is relatively small. Since the formation of flow patterns depends on different variables, four types of flow patterns, namely, stratified flow, slug flow, annular flow, and bubble flow, may occur under changes in experimental conditions, such as gas-liquid flow rate, gas content, temperature, and diameter of the pipe. To further validate the suggested identification method, more data on the flow patterns across various experimental situations should be gathered in the future.  
In addition, the multi-sensor feature fusion method in this study performs direct convolution to extract features from the flow type signal and ECT image. However, it simplifies the feature extraction process and improves the experimental efficiency. It also allows deep mining of the flow type vibration information. The original vibration signal can be subjected to techniques such as FFT and wavelet transform to turn it into a two-dimensional picture. Then, the two-dimensional image features can be extracted and combined with the ECT image data for recognition. By this method, it is verified whether the recognition accuracy can be further improved.  
Figure 19: Results of training several models are compared.