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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
F1000 Research Limited
2022
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| Materias: | |
| 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 |