A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations
This work addresses the need for fast and consistent analysis of TEM videos in materials science, though it is incremental as it applies existing object detection methods to a new domain.
The authors tackled the problem of manual analysis of Transmission Electron Microscopy (TEM) videos, which is tedious and error-prone, by developing an automated system using YOLO for defect detection and tracking, achieving an F1 score of 0.89 for frame-level analysis and enabling high-fidelity evaluation of defect evolution.
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.