Wang, P., Lin, Y., Muroiwa, R. et al. (2 more authors) (2020) A weighted variance approach for uncertainty quantification in high quality steel rolling. In: Proceedings of 2020 IEEE 23rd International Conference on Information Fusion (FUSION). 2020 IEEE 23rd International Conference on Information Fusion (FUSION), 06-09 Jul 2020, Rustenburg, South Africa. IEEE , pp. 1-7. ISBN 9781728168302
Abstract
This paper proposes a computer vision framework aimed to segment hot steel sections and contribute to rolling precision. The steel section dimensions are calculated for the purposes of automating a high temperature rolling process. A structured forest algorithm along with the developed steel bar edge detection and regression algorithms extract the edges of the high temperature bars in optical videos captured by a GoPro® camera. To quantify the impact of noises that affect the segmentation process and the final diameter measurements, a weighted variance is calculated, providing a level of trust in the measurements. The results show an accuracy which is in line with the rolling standards, i.e. with a root mean square error less than 2.5 mm.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Manufacturing and automation; Metrology; Computer vision; High temperature steel production; Uncertainty quantification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jun 2020 07:01 |
Last Modified: | 10 Sep 2021 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/FUSION45008.2020.9190527 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161390 |