用SAM2模型实现复杂气泡的精准分割，仅需100张标注图。
Segmenting the Complex and Irregular in Two-Phase Flows: A Real-World Empirical Study with SAM2
- 基于SAM2模型进行微调，解决非球形气泡分割难题。
- 仅用100张标注图像，即可准确分割不规则气泡结构。
- 适用于冶金、减阻等工业场景中的复杂多相流分析。
在从冶金加工到船舶减阻的众多工业场景中，多相流中气泡的分割是一个关键却未解决的挑战。传统方法及多数近期学习方法假设气泡近似球形，难以应对气泡变形、合并或破裂的情况。这一复杂性在气膜润滑系统中尤为明显，合并后的气泡形成无定形且拓扑多样的斑块。本文从现代视觉基础模型的角度重新审视该问题，将任务视为迁移学习问题，并首次证明：经过微调的Segment Anything Model SAM v2.1 可仅使用100张标注图像，准确分割高度非凸、形状不规则的气泡结构。
原文摘要 · Abstract (English)
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.
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