We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures.
- Authors
Tellaeche Iglesias, Alberto; Campos Anaya, Miguel Ángel; Pajares Martinsanz, Gonzalo; Pastor-López, Iker; Hussain, Zahir M.
- Abstract
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, labeling 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, outperforming results of previous works.
- Subjects
SUPPORT vector machines; IMAGE sensors; TEXTURES; IMAGE processing
- Publication
Sensors (14248220), 2021, Vol 21, Issue 10, p3339
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21103339