Accurate detection and analysis of bubble size and shape in bubbly flow are critical to understanding mass and heat transfer processes. Convolutional neural networks have limitations in different bubble images due to their dependence on large amounts of labeled data. A new foundational Segment Anything Model (SAM) recently attracts lots of attention for its zero-shot segmentation performance. Herein, we developed a novel image processing method named bubSAM, which achieves efficient and accurate bubble segmentation and shape reconstruction based on SAM. The segmentation performance of bubSAM is 30% higher than that of SAM, and its accuracy reaches 90% under different bubbly flow conditions. The accuracy of bubble shape reconstruction (BSR) algorithm in bubSAM is about 30% higher than that of typical ellipse fitting method, thus better restoring the geometric shape of bubbles. BubSAM can provide great support for understanding gas–liquid multiphase flow and design of industrial multiphase reactors.

中文翻译：

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BubSAM：基于气泡流Segment Anything Model的气泡分割和形状重建


准确检测和分析气泡流中的气泡尺寸和形状对于理解传质和传热过程至关重要。卷积神经网络由于依赖于大​​量标记数据，因此在不同的气泡图像中存在局限性。一种新的基础分段任意模型（SAM）最近因其零样本分段性能而引起了广泛关注。在此，我们开发了一种新颖的图像处理方法bubSAM，该方法基于SAM实现高效、准确的气泡分割和形状重建。 bubSAM的分割性能比SAM高出30%，在不同的气泡流条件下其准确率达到90%。 bubSAM中的气泡形状重建（BSR）算法的精度比典型的椭圆拟合方法提高了约30%，从而更好地恢复了气泡的几何形状。 BubSAM可以为理解气液多相流和工业多相反应器的设计提供很大的支持。