  In this work, we perform semantic segmentation of multiple defect types in
electron microscopy images of irradiated FeCrAl alloys using a deep learning
Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an
in-depth analysis of key model performance statistics, with a focus on
quantities such as predicted distributions of defect shapes, defect sizes, and
defect areal densities relevant to informing modeling and understanding of
irradiated Fe-based materials properties. To better understand the performance
and present limitations of the model, we provide examples of useful evaluation
tests which include a suite of random splits, and dataset size-dependent and
domain-targeted cross validation tests. Overall, we find that the current model
is a fast, effective tool for automatically characterizing and quantifying
multiple defect types in microscopy images, with a level of accuracy on par
with human domain expert labelers. More specifically, the model can achieve
average defect identification F1 scores as high as 0.8, and, based on random
cross validation, have low overall average (+/- standard deviation) defect size
and density percentage errors of 7.3 (+/- 3.8)% and 12.7 (+/- 5.3)%,
respectively. Further, our model predicts the expected material hardening to
within 10-20 MPa (about 10% of total hardening), which is about the same error
level as experiments. Our targeted evaluation tests also suggest the best path
toward improving future models is not expanding existing databases with more
labeled images but instead data additions that target weak points of the model
domain, such as images from different microscopes, imaging conditions,
irradiation environments, and alloy types. Finally, we discuss the first phase
of an effort to provide an easy-to-use, open-source object detection tool to
the broader community for identifying defects in new images.
