A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification-Reference-Cited by-同舟云学术

A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification

Published:2018 Issue: Volume:72 Page:1069-1072
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

Chen Wen,Gao Yiping,Gao Liang,Li Xinyu

Publisher

Elsevier BV

Subject

General Medicine

Reference20 articles.

1. Review of vision-based steel surface inspection systems;Neogi;EURASIP Journal on Image and Video Processing,2014

2. Hierarchical classification of surface defects on dusty wood boards;Kim;Pattern Recognition Letters,1994

3. Color and texture based wood inspection with non-supervised clustering;Niskanen;In Proceedings of the scandinavian Conference on image analysis,2001

4. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection;Weimer;CIRP Annals-Manufacturing Technology,2016

5. Defect detection for corner cracks in steel billets using a wavelet reconstruction method;Jeon;JOSA A,2014

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