  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.

Computer Vision and Pattern Recognition;Materials Science