## 1 Introduction
Sensor-based approaches can be cumbersome, requiring intricate installation and maintenance, especially as the flow condition becomes more chaotic, leading to noticeable signal interference. Alternatively, employing image processing techniques can greatly improve the convenience of measurement methods. However, current applications primarily focus on two-phase flow pattern classification in research on gas-liquid two-phase flow pattern classifications in research on gas-liquid two-phase flow patterns within an S-shaped watertight pipe, achieving detection and validation accuracies of 98.13% and 98.06%, respectively. [PERSON] et al. [10] utilized the GoogLeNet+5 algorithmic model for flow pattern recognition on image datasets of various liquid-liquid two-phase flows such as Nalgioil and GaInSn-Water, achieving training and testing accuracies of 95.09% and 98.12%, respectively. [PERSON] et al. [11] utilized CNN algorithms to classify flow patterns in images of methane and tetrafluoromethane within horizontal circular pipes, achieving a testing dataset accuracy exceeding 90.63% and an average accuracy surpassing 97.56% for all data points in the database. [PERSON] et al. [12] designed a branch-aggregation network (BAN) for classifying flow patterns in gas-liquid two-phase flow images, achieving a fast convergence speed and a recognition accuracy of 99.60%, highlighting its advantage in noise resistance. [PERSON] et al. [13] proposed a deep learning method based on complex networks that combined the original signals of limited penetrable visibility graphs (LPVG) with images for flow pattern classification and gas void fraction measurement. Their results showed a classification accuracy of 95.3% and an average root mean square error (RMSE) of 0.0038, with an average absolute percentage error of 6.3% for gas void fraction measurement. [PERSON] et al. [14] utilized computer vision techniques and deep learning to train CNNs and long short-term memory (LSTM) networks for classifying fluid flow states using video frames as features. They also measured steam mass flow rates within the range of (0.005 to 0.023) kg/s, with an average RMSE of 5% of full scale, achieving a classification accuracy of 92%. There are also notable research outcomes in gas-liquid two-phase flow pattern classifications utilizing non-image processing methods combined with intelligent learning models. [PERSON] et al. [15] employed ultrasonic sensors and a convolutional recurrent neural network  
network (CRNN) to identify gas-liquid two-phase flow patterns within an S-shaped water-tight pipe, achieving detection and validation accuracies of 98.13% and 98.06%, respectively. [PERSON] et al. (2016) proposed a deep neural network framework to utilize acquired signals. By employing two slicing operations combined with a bidirectional long-short memory network (BiLSTM) and a CNN, they attained an average recognition accuracy of 99.32%. [PERSON] et al. (2017) utilized conductivity probe signals as input data to identify the flow state of boiling flow in vertical annular channels and proposed a two-step method using unsupervised self-organizing map (SOM) to recognize local and global flow states, achieving a classification accuracy of over 90%. [PERSON] et al. (2018) introduced a method for classifying flow states using deep neural networks (DNNs), which operate on features extracted from Doppler ultrasound signals using fast Fourier transform (FFT). Experimental results demonstrated that DNNs achieved a classification accuracy of 99.01%. Additionally, [PERSON] et al. (2019) proposed a new method for feature extraction from preprocessed data, named belt-shaped features (BSFs), which were applied to ConvNet classifiers, achieving accuracy rates of 97.40%, 94.57%, and 94.94% for the training set, test set, and validation set, respectively. [PERSON] et al. (2018) selected 12 databases to train and test machine learning models, with experimental results showing that the extra trees model classified flow patterns with the highest fidelity, achieving an accuracy of 98.8%.