A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling
A Weighted Variance Approach for Uncertainty Quantification in High Quality Steel Rolling
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DOI：
10.23919/fusion45008.2020.9190527
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Peng Wang;Yueda Lin;R. Muroiwa;S. Pike;L. Mihaylova
中科院分区：
文献类型：
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作者：
Peng Wang;Yueda Lin;R. Muroiwa;S. Pike;L. Mihaylova
本文提出了一种计算机视觉框架，旨在分割热钢型材，并有助于轧制精度。钢截面尺寸的计算是为了使高温轧制过程自动化。结构化森林算法沿着开发的钢筋边缘检测和回归算法提取GoPro®摄像头拍摄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的噪声的影响，计算加权方差，从而提供测量的可信度。结果表明，精度符合轧制标准，即均方根误差小于2.5 mm。
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.