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Title: A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling
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 GoPror 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.
Authors:
; ; ; ;
Award ID(s):
1903466
Publication Date:
NSF-PAR ID:
10161400
Journal Name:
Proceedings of the International Conference on Information Fusion (Fusion 2020)
Sponsoring Org:
National Science Foundation
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