Experimental Measurement of Ice-Curling Stone Friction Coefficient Based on Computer Vision Technology: A Case Study of "Ice Cube" for 2022 Beijing Winter Olympics

被引：1

作者：

Li, Junxing ^{[1
,2
]}

Li, Shuaiyu ^{[1
,2
]}

Zhang, Wenyuan ^{[1
,2
]}

Wei, Bo ^{[3
]}

Yang, Qiyong ^{[4
]}

机构：

[1] Harbin Inst Technol, Key Lab Struct Dynam Behav & Control, Minist Educ, Harbin 150090, Peoples R China

[2] Harbin Inst Technol, Key Lab Smart Prevent & Mitigat Civil Engn Disast, Minist Ind & Informat Technol, Harbin 150090, Peoples R China

[3] CCCC First Harbor Engn Co Ltd, Tianjin Port Engn Inst Co Ltd, Tianjin 300222, Peoples R China

[4] Beijing Natl Aquat Ctr Co Ltd, Beijing 100101, Peoples R China

来源：

LUBRICANTS | 2022年 / 10卷 / 10期

关键词：

curling stone; coefficient of friction; on-site measurement; computer vision technology; sensor-based method; Beijing Winter Olympics; PIVOT-SLIDE MODEL; DYNAMICS; MOTION; MECHANISM;

D O I：

10.3390/lubricants10100265

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone-ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone-ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to determine the location of the curling stone was proposed using YOLO-V3 (You Only Look Once, Version 3) deep neural networks and the CSRT Object tracking algorithm. Video data was recorded during the curling stone throwing experiments, and the friction coefficient was extracted. Furthermore, the influence of the sliding velocity on the friction coefficient was discussed. Comparison with published experimental data and models and verification of the obtained results, using a sensor-based method, were conducted. Results show that the coefficient of friction (ranging from 0.006 to 0.016) decreased with increasing sliding velocity, due to the presence of a liquid-like layer. Our obtained results were consistent with the literature data and the friction model of Lozowski. In addition, the experimental results of the computer vision technique method and the accelerometer sensor method showed remarkable agreement, supporting the accuracy and reliability of our proposed measurement procedure based on deep learning.

引用

页数：15