On combining convolutional autoencoders and support vector machines for fault detection in industrial textures
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Fecha
2021-05-02
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MDPI AG
Resumen
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.
Palabras clave
Autoencoder
Hybridization
Image sensors
SVM
Texture inspection
Hybridization
Image sensors
SVM
Texture inspection
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Materias
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Tellaeche Iglesias, A., Campos Anaya, M. Á., Pajares Martinsanz, G., & Pastor‐lópez, I. (2021). On combining convolutional autoencoders and support vector machines for fault detection in industrial textures. Sensors, 21(10). https://doi.org/10.3390/S21103339