Examinando por Autor "Pajares Martinsanz, Gonzalo"

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    On combining convolutional autoencoders and support vector machines for fault detection in industrial textures
    (MDPI AG, 2021-05-02) Tellaeche Iglesias, Alberto ; Campos Anaya, Miguel Ángel; Pajares Martinsanz, Gonzalo; Pastor López, Iker
    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 trained using only defect free textured images for each type of analyzed texture, label-ing the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, out-performing results of previous works.