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

机译：高质量钢轧制中不确定度量化的加权方差法

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摘要

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.
机译：本文提出了一种计算机视觉框架，旨在分割热钢段并有助于轧制精度。计算钢截面尺寸是为了实现高温轧制过程的自动化。结构化的森林算法以及已开发的钢筋边缘检测和回归算法可提取GoPro®摄像机捕获的光学视频中高温钢筋的边缘。为了量化影响分割过程和最终直径测量结果的噪声的影响，需要计算加权方差，以提供对测量结果的信任度。结果表明精度与滚动标准相符，即均方根误差小于2.5mm。

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