Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels

Due to the large intra-class variations and unbalanced training samples, the accuracy of existing algorithms used in defect classification of hot rolled steels is unsatisfactory. In this paper, a new hierarchical learning framework is proposed based on convolutional neural networks to classify hot r...

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Veröffentlicht in:	Image and vision computing Jg. 89; S. 12 - 20
Hauptverfasser:	He, Di, Xu, Ke, Wang, Dadong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2019
Schlagworte:	AutoEncoder Convolutional neural networks Defect identification Hot rolled steels Surface inspection Defect identification Surface inspection Hot rolled steels Convolutional neural networks AutoEncoder
ISSN:	0262-8856, 1872-8138
Online-Zugang:	Volltext
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