Abstract： To solve the difficulty of flow pattern recognition for underwater gas jet, a flow pattern recognition method was proposed based on direct digital image processing and convolutional neural network. The original image dataset was directly obtained by the underwater gas jet experimental system, and the flow control and acquisition system was used to obtain the underwater jet shapes under different operating conditions. The image was preprocessed, and the unnecessary information in the image was removed through image cropping, while the noise in the image was suppressed by the two-dimensional median filtering algorithm. The maximum inter class variance algorithm was used for binarization to provide a direct bubble image dataset for flow pattern recognition in convolutional neural networks. Three different classical convolutional neural networks of LeNet, AlexNet and ResNet were used to train the processed images of different flow patterns. The classification model was optimized by continuously adjusting parameters, and precision, sensitivity, specificity and accuracy were used as evaluation indicators for underwater jet flow pattern classification. The results show that ResNet can achieve the highest precision, sensitivity and specificity in the transition zone and jet zone, respectively. ResNet can achieve the best precision and specificity in the bubble flow zone. ResNet has the best efficiency for identifying convective patterns with recognition accuracy of 96.2%.
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2023, Vol. 44 
