Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

被引:8
作者
Huang, Jinke [1 ]
Shao, Xinxing [1 ]
Yang, Fujun [1 ]
Zhu, Jianguo [2 ]
He, Xiaoyuan [1 ]
机构
[1] Southeast Univ, Sch Civil Engn, Dept Engn Mech, Nanjing, Peoples R China
[2] Jiangsu Univ, Fac Civil Engn & Mech, Zhenjiang, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
bridge deflection; computer vision; photogrammetry; camera calibration; feature detection; feature matching; DIGITAL IMAGE CORRELATION; DISPLACEMENT MEASUREMENT; SYSTEMATIC-ERROR; REAL-TIME; CALIBRATION; PERFORMANCE; DISTORTION; NONCONTACT; ROTATION; NOISE;
D O I
10.1117/1.OE.61.7.070901
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The deflection that can reflect the vertical stiffness of a bridge plays an important role in the structural evaluation and health monitoring of bridges. In the past 20 years, the bridge deflection measurement methods based on computer vision and photogrammetry have been gradually applied to the field measurement due to the advantages of noncontact measurement, simple experimental setup, and easy installation. The technical research progress of vision-based bridge deflection measurement is reported from four aspects: basic principles, measurement methods, influencing factors, and applications. Basic principles mainly include camera calibration, three-dimensional (3D) stereo vision, photogrammetry, feature detection, and matching. For measurement methods, the single-camera two-dimensional measurement, the dual-camera 3D measurement, the quasistatic measurement based on photogrammetry, the multipoint dynamic measurement based on the displacement-relay videometrics and the deflection measurement based on UAV platform are introduced, respectively. In the section of influencing factors, this part summarizes the work of many researchers on the effects of camera imaging factors, calibration factors, algorithm factors, and environmental factors on measurement results. The field measurement results at different measurement distances and measurement accuracy based on these are presented in terms of applications. Finally, the future development trends of vision-based bridge deflection measurement are expected. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:26
相关论文
共 83 条
  • [1] Nonmetric calibration of camera lens distortion: Differential methods and robust estimation
    Ahmed, M
    Farag, A
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, 14 (08) : 1215 - 1230
  • [2] Field Deployment and Laboratory Evaluation of 2D Digital Image Correlation for Deflection Sensing in Complex Environments
    Alipour, Mohamad
    Washlesky, Savannah J.
    Harris, Devin K.
    [J]. JOURNAL OF BRIDGE ENGINEERING, 2019, 24 (04)
  • [3] Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion
    Anantrasirichai, Nantheera
    Achim, Alin
    Kingsbury, Nick G.
    Bull, David R.
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (06) : 2398 - 2408
  • [4] Influence of Camera Rotation on Stereo-DIC and Compensation Methods
    Balcaen, R.
    Reu, P. L.
    Lava, P.
    Debruyne, D.
    [J]. EXPERIMENTAL MECHANICS, 2018, 58 (07) : 1101 - 1114
  • [5] Speeded-Up Robust Features (SURF)
    Bay, Herbert
    Ess, Andreas
    Tuytelaars, Tinne
    Van Gool, Luc
    [J]. COMPUTER VISION AND IMAGE UNDERSTANDING, 2008, 110 (03) : 346 - 359
  • [6] Monitoring of structures using the global positioning system
    Brown, CJ
    Karuna, R
    Ashkenazi, V
    Roberts, GW
    Evans, RA
    [J]. PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-STRUCTURES AND BUILDINGS, 1999, 134 (01) : 97 - 105
  • [7] Robust Principal Component Analysis?
    Candes, Emmanuel J.
    Li, Xiaodong
    Ma, Yi
    Wright, John
    [J]. JOURNAL OF THE ACM, 2011, 58 (03)
  • [8] A digital image correlation-aided sampling moir′e method for high-accurate in-plane displacement measurements
    Chen, Chang-Fu
    Mao, Feng-Shan
    Yu, Jia-Yong
    [J]. MEASUREMENT, 2021, 182
  • [9] Homography-based measurement of bridge vibration using UAV and DIC method
    Chen, Gongfa
    Liang, Qiang
    Zhong, Wentao
    Gao, Xingjun
    Cui, Fangsen
    [J]. MEASUREMENT, 2021, 170
  • [10] A Content-Aware Image Prior
    Cho, Taeg Sang
    Joshi, Neel
    Zitnick, C. Lawrence
    Kang, Sing Bing
    Szeliski, Richard
    Freeman, William T.
    [J]. 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2010, : 169 - 176