  We propose instance segmentation as a useful tool for image analysis in
materials science. Instance segmentation is an advanced technique in computer
vision which generates individual segmentation masks for every object of
interest that is recognized in an image. Using an out-of-the-box implementation
of Mask R-CNN, instance segmentation is applied to images of metal powder
particles produced through gas atomization. Leveraging transfer learning allows
for the analysis to be conducted with a very small training set of labeled
images. As well as providing another method for measuring the particle size
distribution, we demonstrate the first direct measurements of the satellite
content in powder samples. After analyzing the results for the labeled data
dataset, the trained model was used to generate measurements for a much larger
set of unlabeled images. The resulting particle size measurements showed
reasonable agreement with laser scattering measurements. The satellite
measurements were self-consistent and showed good agreement with the expected
trends for different samples. Finally, we provide a small case study showing
how instance segmentation can be used to measure spheroidite content in the
UltraHigh Carbon Steel Database, demonstrating the flexibility of the
technique.
