Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Author:

Agarwal S.,Sawant A.ORCID,Faisal M.ORCID,Copp S.E.ORCID,Reyes-Zacarias J.,Lin Yan-RuORCID,Zinkle S.J.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference21 articles.

1. Helium mobility in advanced nuclear ceramics;Agarwal;Nucl. Instrum. Methods Phys. Res. B,2014

2. Automated detection of helium bubbles in irradiated X-750;Anderson;Ultramicroscopy,2020

3. Visual geometry group - University of Oxford;Anon,2022

4. SegNet: A deep convolutional encoder–decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

5. Distance map loss penalty term for semantic segmentation;Calivá,2019

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