On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures

Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are t...

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Detalles Bibliográficos
Autores principales: Tellaeche Iglesias, Alberto, Campos Anaya, Miguel Ángel, Pajares Martinsanz, Gonzalo, Pastor-López, Iker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150843/
https://www.ncbi.nlm.nih.gov/pubmed/34064975
http://dx.doi.org/10.3390/s21103339

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150843/
https://www.ncbi.nlm.nih.gov/pubmed/34064975
http://dx.doi.org/10.3390/s21103339

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