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A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines
dc.contributor.author | Cañero-Nieto, Juan Miguel | |
dc.contributor.author | Solano-Martos, José Francisco | |
dc.contributor.author | Martín-Fernández, Francisco de Sales | |
dc.date.accessioned | 2019-07-15T07:12:33Z | |
dc.date.available | 2019-07-15T07:12:33Z | |
dc.date.created | 2019 | |
dc.date.issued | 2019-07-15 | |
dc.identifier.uri | https://hdl.handle.net/10630/18045 | |
dc.description.abstract | The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance.. | en_US |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines | en_US |
dc.type | info:eu-repo/semantics/conferenceObject | en_US |
dc.centro | Escuela de Ingenierías Industriales | en_US |
dc.relation.eventtitle | 8th Manufacturing Engineering Society International Conference | en_US |
dc.relation.eventplace | Madrid, España | en_US |
dc.relation.eventdate | 19/06/2019 | en_US |
dc.rights.cc | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |