|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Towards Deep Learning of Dislocations from TEM Images: The Problem of “Never Enough Training Data”
||Kishan Govind, Marc Legros, Stefan Sandfeld, Daniela Oliveros
|On-Site Speaker (Planned)
Transmission electron microscopy (TEM) data generated in the form of bright field screenshots of microstructure can be used directly to study dislocations in great detail but is limited by the difficulty in identifying and extracting these individual defects. We attempt to automate the study of dislocations present in TEM images using a deep learning network, U-Net which can segment dislocations. Here we present a parametric model to generate synthetic training data for the supervised machine learning task of dislocation segmentation. Experimentation with different synthetic datasets which vary in dislocation microstructure as well as background for synthetic images suggest that a general synthetic data which requires very little input from real data can give better results. This study shows the importance of using synthetic training data to help studying large and real TEM data in great detail.