Surface defect classification of steels with a new semi-supervised learning method

•A semi-supervised learning method named CAE-SGAN is proposed to classify surface defects of steels.•CAE-SGAN improves the performance of SGAN with limited training samples.•When training the discriminator of SGAN, the decoder network of CAE is not truncated.•CAE-SGAN is tested with sample images co...

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Bibliographic Details

Published in:	Optics and lasers in engineering Vol. 117; pp. 40 - 48
Main Authors:	Di, He, Ke, Xu, Peng, Zhou, Dongdong, Zhou
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.06.2019
Subjects:	Convolutional autoencoder Defect detection Generative adversarial networks Semi-supervised learning Surface inspection Generative adversarial networks Surface inspection Semi-supervised learning Convolutional autoencoder Defect detection
ISSN:	0143-8166, 1873-0302
Online Access:	Get full text
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