Abstract
Segmenting gas bubbles in multiphase flows is a critical yet unsolved challenge in numerous industrial settings, from metallurgical processing to maritime drag reduction. Traditional approaches-and most recent learning-based methods-assume near-spherical shapes, limiting their effectiveness in regimes where bubbles undergo deformation, coalescence, or breakup. This complexity is particularly evident in air lubrication systems, where coalesced bubbles form amorphous and topologically diverse patches. In this work, we revisit the problem through the lens of modern vision foundation models. We cast the task as a transfer learning problem and demonstrate, for the first time, that a fine-tuned Segment Anything Model SAM v2.1 can accurately segment highly non-convex, irregular bubble structures using as few as 100 annotated images.
Abstract (translated)
在多相流中的气泡分割是许多工业环境中的一个关键但尚未解决的挑战，从冶金加工到减少海洋阻力。传统方法和最近基于学习的方法都假设气泡形状接近球形，这限制了它们在气泡发生变形、合并或破裂的情况下的有效性。这种复杂性尤其体现在空气润滑系统中，在那里合并后的气泡形成了无定形且拓扑多样的区域。 在这项工作中，我们通过现代视觉基础模型的视角重新审视这个问题。我们将任务视为一个迁移学习问题，并首次证明，通过对Segment Anything Model（SAM）v2.1进行微调，可以仅使用100张标注图像准确地分割出高度非凸和不规则形状的气泡结构。 这段话强调了在多相流中气泡复杂几何形态所带来的挑战，并介绍了如何利用现代视觉基础模型及迁移学习技术来有效解决这一问题。
URL
https://arxiv.org/abs/2508.05227
