用AI自动估算金属显微图像的晶粒尺寸，符合行业标准。
Bridging Foundation Models and ASTM Metallurgical Standards for Automated Grain Size Estimation from Microscopy Images
- 将Cellpose-SAM适配微结构，结合拓扑感知梯度追踪与标准计数模块。
- 仅用2个样本训练，晶粒等级预测MAPE低至1.50%，符合50粒采样标准。
- 适合材料科研与工业质检，实现高精度自动化晶粒分析。
从显微图像中提取标准化冶金指标仍面临晶粒形态复杂和监督分割数据需求高的挑战。为弥合基础计算机视觉与实际冶金评估之间的差距，我们提出一种自动化的密集实例分割与晶粒尺寸估计流程，将Cellpose-SAM适配于微结构，并整合其拓扑感知梯度追踪与ASTM E112 Jeffries平面计数模块。我们系统性地将该流程与经典卷积网络（U-Net）、自适应提示视觉基础模型（MatSAM）及当代视觉语言模型（Qwen2.5-VL-7B）进行对比。结果表明，尽管开箱即用的视觉语言模型在密集显微计数所需的局部空间推理上表现不佳，且MatSAM虽有领域特定提示生成但仍存在过分割问题，而我们的适配流程成功保持了拓扑分离。此外，在逐步减少的训练集上实验显示出色的少样本可扩展性：仅使用两个训练样本，系统对ASTM晶粒等级（G）的预测平均绝对百分比误差（MAPE）低至1.50%；跨不同目标晶粒数量的鲁棒性测试也实证验证了ASTM 50粒采样的最低要求。这些结果凸显了面向应用的基础模型集成在高精度、自动化材料表征中的有效性。项目代码仓库见：https://github.com/mueez-overflow/ASTM-Grain-Size-Estimator。
原文摘要 · Abstract (English)
Extracting standardized metallurgical metrics from microscopy images remains challenging due to complex grain morphology and the data demands of supervised segmentation. To bridge foundational computer vision with practical metallurgical evaluation, we propose an automated pipeline for dense instance segmentation and grain size estimation that adapts Cellpose-SAM to microstructures and integrates its topology-aware gradient tracking with an ASTM E112 Jeffries planimetric module. We systematically benchmark this pipeline against a classical convolutional network (U-Net), an adaptive-prompting vision foundation model (MatSAM) and a contemporary vision-language model (Qwen2.5-VL-7B). Our evaluations reveal that while the out-of-the-box vision-language model struggles with the localized spatial reasoning required for dense microscopic counting and MatSAM suffers from over-segmentation despite its domain-specific prompt generation, our adapted pipeline successfully maintains topological separation. Furthermore, experiments across progressively reduced training splits demonstrate exceptional few-shot scalability; utilizing only two training samples, the proposed system predicts the ASTM grain size number (G) with a mean absolute percentage error (MAPE) as low as 1.50%, while robustness testing across varying target grain counts empirically validates the ASTM 50-grain sampling minimum. These results highlight the efficacy of application-level foundation model integration for highly accurate, automated materials characterization. Our project repository is available at https://github.com/mueez-overflow/ASTM-Grain-Size-Estimator.
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