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.

翻译：原位拉伸实验中的定量透射电子显微镜（TEM）能够揭示位错（金属晶格中的线状缺陷）的运动。在材料科学领域，了解位错的位置和运动对于创制具有优异性能的新材料至关重要。然而，一个长期存在的难题是识别位错位置并提取其形状，这最终将有助于建立此类材料的数字孪生。本研究定量比较了包括Mask R-CNN和YOLOv8在内的最新实例分割方法。我们将实例分割得到的位错掩膜转换为数学曲线，从而实现对位错长度和几何形态的定量分析——这对领域科学家而言是重要信息，并据此提出一种新型长度感知质量指标，用于评估网络性能。我们的分割流水线展现出适用于所有领域特定后处理的高精度。此外，基于物理学的指标比通常使用的像素级指标表现更为稳定。