Download PDFOpen PDF in browserA Weighted Variance Approach for Uncertainty Quantification in High Quality Steel RollingEasyChair Preprint 35737 pages•Date: June 7, 2020AbstractThis 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: High temperature steel production, Manufacturing and Automation, Metrology, computer vision, uncertainty quantification
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