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.

中文翻译：

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BubCNN：使用 Faster RCNN 和形状回归网络的气泡检测

摘要 关于气液多相流的详细知识对于优化工业系统很重要。图像处理成像是最常用的测量技术。然而，工作流程和参数在很大程度上取决于实验条件，尚未开发出普遍适用的过程。在这里，提出了一种基于卷积神经网络 (CNN) 的工作流程，可以在更广泛的实验条件下使用。该方法名为 BubCNN，采用基于更快区域的 CNN (RCNN) 检测器来定位气泡，并使用形状回归 CNN 来预测气泡形状参数。系统地分析了两个模块的超参数和网络架构。BubCNN 在不同的实验条件下取得了准确的结果。预训练程序已在 GitHub 上公开发布。