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Deep Learning-based Computer Vision for Radiation Defect Analysis: from Static Defect Segmentation to Dynamic Defect Tracking

Published online by Cambridge University Press:  30 July 2021

Rajat Sainju ,
Wei-Ying Chen ,
Samuel Schaefer ,
Graham Roberts ,
Mychailo Toloczko ,
Danny Edwards ,
Meimei Li  and
Yuanyuan Zhu
Rajat Sainju
Affiliation:
University of Connecticut, United States
Wei-Ying Chen
Affiliation:
ANL, United States
Samuel Schaefer
Affiliation:
UConn, United States
Graham Roberts
Affiliation:
UConn, United States
Mychailo Toloczko
Affiliation:
PNNL, United States
Danny Edwards
Affiliation:
PNNL, United States
Meimei Li
Affiliation:
ANL, United States
Yuanyuan Zhu
Affiliation:
University of Connecticut, Storrs, Connecticut, United States

Abstract

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Type
Defects in Materials: How We See and Understand Them
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1464 - 1465
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Zhu, Y, Ophus, C, Toloczko, MB and Edwards, DJ, Ultramicroscopy, 193(2018) 12-23.CrossRefGoogle Scholar
Roberts, G, Haile, S Y, Sainju, R, Edwards, D J, Hutchinson, B and Zhu, Y, Scientific Reports 9(2019), 12744CrossRefGoogle Scholar
Sakaida, H, Sekimura, N and Ishino, S, Journal of Nuclear Materials, 179(1991) 928-930CrossRefGoogle Scholar