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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (6): 941-947.DOI: 10.11996/JG.j.2095-302X.2021060941

• 图像处理与计算机视觉 • 上一篇    下一篇

基于欧拉运动放大算法的桥梁索力测试方法研究

  

  1. 1. 华南理工大学土木工程与交通学院，广东 广州 510640；  2. 华南理工大学亚热带建筑科学国家重点试验室，广东 广州 510640；  3. 中新国际联合研究院，广东 广州 510555
  • 出版日期:2022-01-18 发布日期:2022-01-18
  • 基金资助:
    广东省自然科学基金项目(2018A030310363，2017A030313393)；广州市科技计划重点项目(20181003SF0059) 

A eulerian video magnification based cable tension identification method for bridge structures 

  1. 1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510640, China;  2. State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou Guangdong 510640, China;  3. Sino-Singapore International Joint Research Institute, Guangzhou Guangdong 510555, China
  • Online:2022-01-18 Published:2022-01-18
  • Supported by:
    Natural Science Foundation of Guangdong Province (2018A030310363, 2017A030313393); Guangzhou Science and Technology Program key Project (20181003SF0059) 

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摘要： 斜拉索、吊杆和系杆是缆索承重桥梁的重要受力构件，但传统的索力测试方法存在操作复杂、 成本高和效率低的缺点，为此开展基于欧拉运动放大算法的索力测试方法研究。采用数码摄像设备采集拉索振 动的数字图像数据，经过基于相位的欧拉运动放大算法处理，获取运动放大后的图像序列；利用基于 Canny 算子的边缘识别算法得到边缘图像序列，并通过边缘定位提取拉索人工标记的位移时程数据；最后利用频率 法识别拉索索力。以拱桥吊杆索力测试为背景，讨论了基于相位的欧拉运动放大算法与基于 Canny 算子的边 缘识别算法的适用情况。与传统基于加速度传感器的索力测试方法相比，该方法具有易操作、低成本和高效 率的特点。

关键词: 桥梁工程, 索力测试, 计算机视觉, 欧拉运动放大, 频率法

Abstract: Cables, booms, and ties are important load-bearing elements of cable-bearing bridges. However, the traditional cable tension identification methods have the disadvantages of complicated operation, high cost, and low efficiency. This paper proposed a cable tension identification method for bridge structure based on Eulerian video magnification. The digital image of cable vibration collected by digital camera was processed by the phase-based Eulerian motion magnification, after which the image sequence after motion magnification was obtained. The edge image sequence was obtained by edge recognition algorithm based on Canny operator, and the displacement data of the mark point was extracted by edge positioning. Finally, the cable force was identified by frequency method. The applicability of the phase-based Eulerian motion magnification algorithm and the edge recognition algorithm based on Canny operator was discussed in the context of the arch bridge boom cable force test. Compared with the traditional acceleration sensor-based cable force testing method, the proposed cable tension identification method for bridge structure based on a motion magnification algorithm has the features of easy operation, low cost, and high efficiency. 

Key words:  , bridge engineering, cable tension identification, computer vision, eulerian video magnification, frequency method 

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