A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations

Abstract

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.

References in corpus (17)

• SSD: Single Shot MultiBox Detector
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• YOLOv3: An Incremental Improvement
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• Machine learning phases of matter
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Opportunities and Challenges for Machine Learning in Materials Science
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
• SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
• Deep Learning for Generic Object Detection: A Survey
• You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
• A Data Ecosystem to Support Machine Learning in Materials Science
• Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2
• Assessing Graph-based Deep Learning Models for Predicting Flash Point
• Automated Classification of Helium Ingress in Irradiated X-750
• MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach

Cited by in corpus (4)

• Machine learning in nuclear materials research
• Benchmark tests of atom segmentation deep learning models with a consistent dataset
• Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
• Accelerating Ensemble Error Bar Prediction with Single Models Fits