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A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling

EasyChair Preprint no. 3573

7 pagesDate: June 8, 2020

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.

Keyphrases: computer vision, High temperature steel production, Manufacturing and Automation, Metrology, uncertainty quantification

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:3573,
  author = {Peng Wang and Yueda Lin and Ree Muroiwa and Simon Pike and Lyudmila Mihaylova},
  title = {A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling},
  howpublished = {EasyChair Preprint no. 3573},

  year = {EasyChair, 2020}}
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