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.

本文研究了在原位应力变形实验中利用定量透射电子显微镜（TEM）揭示金属晶格中线性缺陷 —— 位错的运动。通过定量比较现有的实例分割方法，并将位错分割结果转换为数学线条以进行定量分析，我们提出了一种基于长度的新颖质量度量指标，用于估计网络性能。我们的分割方法准确性高且适用于领域特定的后处理，此外，我们提出的物理度量方式在性能上表现更加稳定可靠。