BubCNN: Bubble detection using Faster RCNN and shape regression network

Abstract

Detailed knowledge about gas-liquid multiphase flows is important to optimize industrial systems. Imaging with image processing is the most commonly used measurement technique. However, the workflow and parameters strongly depend on the experimental conditions and no generally applicable process has been developed yet. Here, a workflow based on convolutional neural networks (CNN) is proposed that can be used with a wider range of experimental conditions. The method, named BubCNN, employs a Faster region-based CNN (RCNN) detector to locate bubbles and a shape regression CNN to predict bubble shape parameters. Hyperparameters and network architectures for both modules were systematically analyzed. BubCNN achieved accurate results for different experimental conditions. A pretrained program was made publicly available on GitHub. Since the whole variety of bubble images was not yet captured in the training data set, an additional semi-automatic transfer learning module is provided that allows to customize BubCNN for different images.