A deep learned nanowire segmentation model using synthetic data augmentation

A deep learned nanowire segmentation model using synthetic data augmentation

Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science. Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of t...

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Bibliographic Details
Main Authors: Banerjee, S. (Author), Emami, N. (Author), Lin, B. (Author), Luo, Y. (Author), Santos, D.A (Author), Xu, B.-X (Author)
Format: Article
Language:English
Published: Nature Research 2022
Subjects:
Cathodes
Convolutional neural networks
Data augmentation
Data driven
Deep learning
Feature analysis
Image data
Material science
Nanowires
Optical intensities
Scanning electron microscopy
Segmentation analysis
Segmentation models
Spectromicroscopy
Synthetic data
Vanadium compounds
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