Abstract This work presents a Convolutional Neural Network (CNN) based method for the shape reconstruction of bubbles in bubbly flows using high-speed camera images. The bubble identification and shape reconstruction adopted a methodology based on a set of anchor points and boxes, where a single anchor point is used for different anchor boxes with various sizes. These anchor points are determined, based on the internal features of the bubble images, which are more easily identifiable, in particular, in regions of the images with high bubble overlapping. This makes possible the application of the procedure to high void fraction bubbly flows. For a given anchor point, different ellipsoidal shapes are suggested as bubble shape candidates and are then correctly chosen by a trained CNN. The CNN training used labeled images from air–water system data set and a hyper-parameter analysis was performed to find the best configuration of the CNN architecture. From this optimal CNN architecture, different high-speed camera acquisitions of bubbly flows were analyzed by the CNN-based bubble shape reconstruction method. In order to gain a better comprehension of the method, experiments were conducted in two gas–liquid systems, air–water and air-aqueous glycerol solution, which resulted in different image parameters, such as brightness, contrast and edge definition. The CNN method trained only with air–water data, showed excellent performance in the cases with air-aqueous glycerol, demonstrating its generalization capability. In addition, the results showed that the deep learning method used in this work is able to detect most of the bubbles present in the high-speed camera images, even in dense bubbly flow configurations. The method developed in this work can be used to further analyze bubbly flows and generate experimental data for the implementation and validation of CFD models.

中文翻译：

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开发基于深度学习的图像处理技术，用于密集气泡流中的气泡模式识别和形状重建

摘要 这项工作提出了一种基于卷积神经网络 (CNN) 的方法，用于使用高速相机图像对气泡流中的气泡进行形状重建。气泡识别和形状重建采用基于一组锚点和框的方法，其中单个锚点用于不同大小的不同锚框。这些锚点是基于气泡图像的内部特征确定的，这些特征更容易识别，特别是在具有高气泡重叠的图像区域中。这使得该程序可以应用于高空隙率的气泡流。对于给定的锚点，建议不同的椭圆体形状作为气泡形状候选，然后由训练有素的 CNN 正确选择。CNN 训练使用来自空气-水系统数据集的标记图像，并执行超参数分析以找到 CNN 架构的最佳配置。从这个最佳的 CNN 架构中，通过基于 CNN 的气泡形状重建方法分析了气泡流的不同高速相机采集。为了更好地理解该方法，在空气-水和空气-甘油水溶液这两种气-液系统中进行了实验，这导致了不同的图像参数，如亮度、对比度和边缘清晰度。仅使用空气-水数据训练的 CNN 方法在使用空气-水甘油的情况下表现出优异的性能，证明了其泛化能力。此外，结果表明，这项工作中使用的深度学习方法能够检测到高速摄像机图像中存在的大部分气泡，即使在密集的气泡流配置中也是如此。在这项工作中开发的方法可用于进一步分析气泡流并为 CFD 模型的实施和验证生成实验数据。