| About this Abstract |
| Meeting |
7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
|
| Symposium
|
ICME 2023
|
| Presentation Title |
Application of Deep Learning Object Detection and Image Segmentation Code Such as YOLO and U-Net for Detection of Helium Bubbles and Voids in Nuclear Reactor Materials |
| Author(s) |
Shradha Agarwal, Sydney Copp, July Reyes, Steven Zinkle |
| On-Site Speaker (Planned) |
Shradha Agarwal |
| Abstract Scope |
Analysing micrographs of microstructural features using
transmission electron
microscopy is key for predicting the performance of structural materials in
nuclear reactors. Analysing micrographs is often a very tedious manual
process,
therefore recently many researchers have tried to automate the process by
using
various types of neural network, however, application of these networks still require lot
of
manual work. This paper compares two state-of-the-art neural networks,
YOLO and U-Net to maximize the automation of tasks such as counting of
microstructural features like helium bubbles and voids.
To better understand the accuracies, performance and limitation of each
model,
we conducted robust hyperparameter validation test including suite of
random
splits and datasetsize-dependent and domain-targeted cross-validation
tests. |
| Proceedings Inclusion? |
Planned: Other |