|About this Abstract
||Materials Science & Technology 2019
||Data Science for Material Property Interpretation
||Automated Defect Detection in Electron Microscopy with Machine Learning
||Dane Morgan, Mingren Shen, Wei Li, Kevin Field
|On-Site Speaker (Planned)
Electron microscopy is widely used to explore defects in crystal structures, but human tracking of defects can be time consuming, error prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work we discuss application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that performance comparable to human analysis can be achieved with relatively small training data sets. We explore multiple deep learning methods that provide various features, e.g., fast processing for video and pixel level categorization to simplify defect dimension determination.
||Definite: At-meeting proceedings