Training neural networks on domain randomized simulations for ultrasonic inspection

To overcome the data scarcity problem of machine learning for nondestructive testing, data augmentation is a commonly used strategy. We propose a method to enable training of neural networks exclusively on simulated data. Simulations not only provide a scalable way to generate and access training da...

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Detalles Bibliográficos
Autores principales: Schlachter, Klaus, Felsner, Kastor, Zambal, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2022
Materias:
Method Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446096/
https://www.ncbi.nlm.nih.gov/pubmed/37645298
http://dx.doi.org/10.12688/openreseurope.14358.2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10446096/
https://www.ncbi.nlm.nih.gov/pubmed/37645298
http://dx.doi.org/10.12688/openreseurope.14358.2

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