Surface defects inspection of cold rolled strips based on neural network

Author

Kang, Ge-Wen ; Liu, Hong-Bing

Author_Institution

Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

8

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

5034

Abstract

Defects on the surface of steel strips are main factors to evaluate quality of steel strips, surface inspection is of great importance to improve quality of steel strips. Traditional surface inspection by human inspectors is far from satisfactory. In this paper, an approach to detect surface defects of steel strips based on feed-forward neural network (FFN) is discussed. The experiments show that the method is effective.

Keywords

automatic optical inspection; cold rolling; computer vision; feedforward neural nets; flaw detection; principal component analysis; quality control; singular value decomposition; steel industry; strips; cold rolled steel strip; computer vision; feature extraction; feed-forward neural network; image processing; principal component analysis; quality evaluation; singular value decomposition; surface defects inspection; Feature extraction; Feedforward neural networks; Feedforward systems; Frequency; Gabor filters; Humans; Inspection; Neural networks; Steel; Strips; Cold rolled strips; Defect detection; Image processing; Machine vision; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527830

Filename

1527830