Automated surface inspection system for black resin coated steel

Author

Park, ChangHyun ; Bae, HoMoon ; Yun, JongPil ; Yun, SungWok

Author_Institution

Syst. Res. Group, POSCO, Pohang, South Korea

fYear

2012

fDate

17-21 Oct. 2012

Firstpage

1683

Lastpage

1685

Abstract

This paper presents defect defection and classification methods for back resin coated coil in the steel industry. The detection algorithm is based on second order statistics of images. To discriminate the detected objects into defect classes, we use support vector machine (SVM). The total 20 attributes are extracted from each defect. To select best model for SVM classifier, we search the parameter spaces by grid search method. The experimental results show that the detection rate is over 98% and classification rate is over 90%.

Keywords

image classification; inspection; object detection; production engineering computing; resins; statistics; steel industry; support vector machines; SVM classifier; attribute extraction; automated surface inspection system; black resin coated steel; defect classification methods; defect defection; grid search method; image second order statistics; parameter spaces; steel industry; support vector machine; Coils; Detection algorithms; Image edge detection; Inspection; Resins; Support vector machines; Surface treatment; CCD; Classification; Defect; Inspection; SVM; Surface;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems (ICCAS), 2012 12th International Conference on

Conference_Location

JeJu Island

Print_ISBN

978-1-4673-2247-8

Type

conf

Filename

6393112