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

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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Bibliographic Details
Main Authors: Alberto Tellaeche Iglesias, Miguel Ángel Campos Anaya, Gonzalo Pajares Martinsanz, Iker Pastor-López
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
image sensors
texture inspection
autoencoder
SVM
hybridization
Online Access:https://www.mdpi.com/1424-8220/21/10/3339

Internet

https://www.mdpi.com/1424-8220/21/10/3339

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