Swin–UNet++: A Nested Swin Transformer Architecture for Location Identification and Morphology Segmentation of Dimples on 2.25Cr1Mo0.25V Fractured Surface

The precise identification of micro-features on 2.25Cr1Mo0.25V steel is of great significance for understanding the mechanism of hydrogen embrittlement (HE) and evaluating the alloy’s properties of HE resistance. Presently, the convolution neural network (CNN) of deep learning is widely applied in t...

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Detalles Bibliográficos
Autores principales: Liu, Pan, Song, Yan, Chai, Mengyu, Han, Zelin, Zhang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703304/
https://www.ncbi.nlm.nih.gov/pubmed/34947098
http://dx.doi.org/10.3390/ma14247504

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703304/
https://www.ncbi.nlm.nih.gov/pubmed/34947098
http://dx.doi.org/10.3390/ma14247504

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