BubCNN: Bubble detection using Faster RCNN and shape regression network
BubCNN: Bubble detection using Faster RCNN and shape regression network
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DOI：
10.1016/j.ces.2019.115467
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发表时间：
2020-04-28
影响因子：
4.7
通讯作者：
Pfeifer, Herbert
中科院分区：
文献类型：
--
作者：
Haas, Tim;Schubert, Christian;Pfeifer, Herbert
详细了解气液多相流对优化工业系统非常重要。图像处理成像是最常用的测量技术。但是，工作流程和参数很大程度上取决于实验条件，目前还没有制定出普遍适用的工艺。本文提出了一种基于卷积神经网络（CNN）的工作流，该工作流可用于更广泛的实验条件。该方法被命名为BubCNN，使用基于更快区域的CNN （RCNN）检测器来定位气泡，并使用形状回归CNN来预测气泡的形状参数。系统地分析了两个模块的超参数和网络结构。在不同的实验条件下，BubCNN得到了准确的结果。一个预先训练的程序在GitHub上公开发布。由于训练数据集中还没有捕获到所有种类的气泡图像，因此提供了一个额外的半自动迁移学习模块，允许为不同的图像定制BubCNN。(C) 2020 Elsevier Ltd.版权所有。
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. (C) 2020 Elsevier Ltd. All rights reserved.