TEM图像中位错的实例分割
Instance Segmentation of Dislocations in TEM Images
AI总结：
本研究比较了Mask R-CNN和YOLOv8等实例分割方法在TEM图像位错分析中的性能，提出将位错掩膜转换为数学线条并引入长度感知质量指标，发现该物理指标比像素级指标更一致。
摘要：
原位应变实验期间的定量透射电子显微镜（TEM）能够揭示位错——金属晶格中的线缺陷——的运动。在材料科学领域，有关位错位置和运动的知识对于创造具有优异性能的新型材料至关重要。然而，一个长期存在的问题是识别位错位置并提取其形状，这将最终帮助为此类材料创建数字孪生。在本研究中，我们定量比较了最先进的实例分割方法，包括Mask R-CNN和YOLOv8。作为实例分割结果的位错掩膜被转换为数学线条，从而能够对位错长度和几何形状进行定量分析——这是领域科学家所需的重要信息，我们随后建议将其作为估计网络性能的新型长度感知质量指标。我们的分割流水线展现出适合所有领域特定后续处理的高准确率。此外，我们基于物理的指标表现得比通常使用的像素级指标更加一致。
英文摘要：
Quantitative Transmission Electron Microscopy (TEM) during in-situ straining experiment is able to reveal the motion of dislocations -- linear defects in the crystal lattice of metals. In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties. A long-standing problem, however, is to identify the position and extract the shape of dislocations, which would ultimately help to create a digital twin of such materials. In this work, we quantitatively compare state-of-the-art instance segmentation methods, including Mask R-CNN and YOLOv8. The dislocation masks as the results of the instance segmentation are converted to mathematical lines, enabling quantitative analysis of dislocation length and geometry -- important information for the domain scientist, which we then propose to include as a novel length-aware quality metric for estimating the network performance. Our segmentation pipeline shows a high accuracy suitable for all domain-specific, further post-processing. Additionally, our physics-based metric turns out to perform much more consistently than typically used pixel-wise metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
