eprintid: 661
rev_number: 14
eprint_status: archive
userid: 12
dir: disk0/00/00/06/61
datestamp: 2014-07-02 23:38:39
lastmod: 2015-05-29 20:17:20
status_changed: 2014-07-02 23:38:39
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Balmashnova, E.
creators_name: Bruurmijn, M.
creators_name: Dissanayake, R.
creators_name: Duits, R.
creators_name: Kampmeijer, L.
creators_name: van Noorden, T.
title: Image Recognition of Shape Defects in Hot Steel Rolling
ispublished: pub
subjects: materials
subjects: other
subjects: data
studygroups: esgi84
companyname: Tata Steel
full_text_status: public
abstract: A frequently occurring issue in hot rolling of steel is so-called tail pinching. Prominent features of a pinched tail are ripple-like defects and a pointed tail. In this report two algorithms are presented to detect those features accurately in 2D gray scale images of steel strips. The two ripple detectors are based on the second order Gaussian derivative and the Gabor transform, a localized Fourier transform, yielding the so-called rippleness measures. Additionally a parameter called tail length is defined which indicates to what extent the overall shape of the tail deviates from an ideal rectangular shape. These methods are tested on images from the surface inspection system at Tata Hot Strip Mill 2 in IJmuiden, it is shown that by defining a simple criterion in the feature space spanned by these two parameters a given set of strips can correctly be classified into pinched and non-pinched strips. These promising results open the way for the development of an automatic pinch detection system.
date: 2012
citation:   Balmashnova, E. and Bruurmijn, M. and Dissanayake, R. and Duits, R. and Kampmeijer, L. and van Noorden, T.  (2012) Image Recognition of Shape Defects in Hot Steel Rolling.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/661/1/p2.pdf