A structural 3D displacement measurement method using monocular camera based on multiple feature points tracking

被引:0
作者
Luo, Woqin [1 ]
Gong, Fengzong [1 ]
Song, Mingming [1 ]
Xia, Ye [1 ,2 ,3 ]
机构
[1] Tongji Univ, Sch Civil Engn, Dept Bridge Engn, Shanghai 200092, Peoples R China
[2] Shanghai Qi Zhi Inst, Shanghai 200232, Peoples R China
[3] Tongji Univ, State Key Lab Disaster Reduct Civil Engn, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
Monocular camera; Deep learning; 3D displacement measurement; Depth estimation; Multiple feature points tracking; VISION;
D O I
10.1016/j.measurement.2024.116406
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Structural displacement measurement techniques based on monocular vision have made great strides in the past decades, however, practical applications still face challenges. These challenges include the complexity and cost associated with long-time automatic monitoring, as well as limitations in dynamic displacement tracking. This study introduces an automatic visual method for measuring three-dimensional (3D) displacement using the monocular camera. The method leverages an improved feature points group tracking algorithm designed for long-time image sequences to achieve accurate measurements of both planar and 3D displacements. This technology addresses challenges associated with error accumulation due to extended time series and small sub-pixel vibrations in the tracking algorithm. In addition, the depth estimation of monocular vision is improved, thus improved the accuracy of displacement measurements. To validate the accuracy of the proposed method in displacement measurement, model experiments were conducted on a three-story frame model. In addition, a cable-stayed bridge model was tested to assess the robustness of the method for depth estimation. The results show that the proposed method achieves good results in different scenarios and is able to realize 3D spatial displacement measurement, and the measurement results are basically consistent with the actual values.
引用
收藏
页数:24
相关论文
共 31 条
[1]   Phase-Based Motion Magnification for Structural Vibration Monitoring at a Video Streaming Rate [J].
An, Jae Young ;
Lee, Soo Il .
IEEE ACCESS, 2022, 10 :123423-123435
[2]   Target-less computer vision for traffic signal structure vibration studies [J].
Bartilson, Daniel T. ;
Wieghaus, Kyle T. ;
Hurlebaus, Stefan .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 60-61 :571-582
[3]   Identification of full-field dynamic modes using continuous displacement response estimated from vibrating edge video [J].
Bhowmick, Sutanu ;
Nagarajaiah, Satish .
JOURNAL OF SOUND AND VIBRATION, 2020, 489
[4]   Estimating structural motions in extreme environmental conditions--A dynamic correlation filter based computer vision approach [J].
Cai, Enjian ;
Zhang, Yi ;
Lu, Xinzheng ;
Ji, Xiaodong ;
Gao, Xiang ;
Hou, Jiale ;
Shi, Ji ;
Guo, Wei .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2024, 215
[5]   Large-scale shake table test verification of bridge condition assessment methods [J].
Chen, Yangbo ;
Feng, Maria Q. ;
Soyoz, Serdar .
JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 2008, 134 (07) :1235-1245
[6]  
DeTone D, 2018, Arxiv, DOI arXiv:1712.07629
[7]   Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection - A review [J].
Feng, Dongming ;
Feng, Maria Q. .
ENGINEERING STRUCTURES, 2018, 156 :105-117
[8]   Cable tension force estimate using novel noncontact vision-based sensor [J].
Feng, Dongming ;
Scarangello, Thomas ;
Feng, Maria Q. ;
Ye, Qi .
MEASUREMENT, 2017, 99 :44-52
[9]   Noncontact Dynamic Displacement Measurement of Structures Using a Moving Laser Doppler Vibrometer [J].
Garg, Piyush ;
Moreu, Fernando ;
Ozdagli, Ali ;
Reda Taha, Mahmoud ;
Mascarenas, David .
JOURNAL OF BRIDGE ENGINEERING, 2019, 24 (09)
[10]   An interferometric radar for non-contact measurement of deflections on civil engineering structures: laboratory and full-scale tests [J].
Gentile, Carmelo ;
Bernardini, Giulia .
STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2010, 6 (05) :521-534