  Videos captured using Transmission Electron Microscopy (TEM) can encode
details regarding the morphological and temporal evolution of a material by
taking snapshots of the microstructure sequentially. However, manual analysis
of such video is tedious, error-prone, unreliable, and prohibitively
time-consuming if one wishes to analyze a significant fraction of frames for
even videos of modest length. In this work, we developed an automated TEM video
analysis system for microstructural features based on the advanced object
detection model called YOLO and tested the system on an in-situ ion irradiation
TEM video of dislocation loops formed in a FeCrAl alloy. The system provides
analysis of features observed in TEM including both static and dynamic
properties using the YOLO-based defect detection module coupled to a geometry
analysis module and a dynamic tracking module. Results show that the system can
achieve human comparable performance with an F1 score of 0.89 for fast,
consistent, and scalable frame-level defect analysis. This result is obtained
on a real but exceptionally clean and stable data set and more challenging data
sets may not achieve this performance. The dynamic tracking also enabled
evaluation of individual defect evolution like per defect growth rate at a
fidelity never before achieved using common human analysis methods. Our work
shows that automatically detecting and tracking interesting microstructures and
properties contained in TEM videos is viable and opens new doors for evaluating
materials dynamics.
