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...
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: | |
Online Access: | https://www.mdpi.com/1424-8220/21/10/3339 |
Similar Items
-
Image-Based Detection of Modifications in Assembled PCBs with Deep Convolutional Autoencoders
by: Diulhio Candido de Oliveira, et al.
Published: (2023-01-01) -
A Defect-Inspection System Constructed by Applying Autoencoder with Clustered Latent Vectors and Multi-Thresholding Classification
by: Cheng-Chang Lien, et al.
Published: (2022-02-01) -
NOVEL HYBRID ALGORITHM USING CONVOLUTIONAL AUTOENCODER WITH SVM FOR ELECTRICAL IMPEDANCE TOMOGRAPHY AND ULTRASOUND COMPUTED TOMOGRAPHY
by: Łukasz Maciura, et al.
Published: (2023-06-01) -
Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
by: Cheng-Wei Lei, et al.
Published: (2021-08-01) -
Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder
by: Yi Zhu, et al.
Published: (2022-12-01)