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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Optics and lasers in engineering Jg. 117; S. 40 - 48
Hauptverfasser:	Di, He, Ke, Xu, Peng, Zhou, Dongdong, Zhou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.06.2019
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!