research-article Open Access
- Authors:
- Weidong Wang Department of Civil Engineering Central South University Hunan China Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China
View Profile
- Jun Peng Department of Civil Engineering Central South University Hunan China Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
Center for Railway Infrastructure Smart Monitoring and Management Central South University Hunan China
View Profile
- Wenbo Hu Department of Civil Engineering Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
View Profile
- Jin Wang Department of Civil Engineering Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
View Profile
- Xinyue Xu Department of Civil Engineering Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
View Profile
- Qasim Zaheer Department of Civil Engineering Central South University Hunan China
Department of Civil Engineering Central South University Hunan China
View Profile
- Shi Qiu Department of Civil Engineering Central South University Hunan China Department of Transportation Guangxi Provincial Government Guangxi China
Department of Civil Engineering Central South University Hunan China
Department of Transportation Guangxi Provincial Government Guangxi China
View Profile
Computer-Aided Civil and Infrastructure EngineeringVolume 39Issue 131 July 2024pp 2010–2027https://doi.org/10.1111/mice.13173
Published:23 February 2024Publication History
- 0citation
- 0
- Downloads
Metrics
Total Citations0Total Downloads0Last 12 Months0
Last 6 weeks0
- Get Citation Alerts
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
- Publisher Site
Computer-Aided Civil and Infrastructure Engineering
Volume 39, Issue 13
PreviousArticleNextArticle
Abstract
Abstract
Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1mm when the measurement distance is less than 40m. When the measurement distance is equal to 90m, the measurement error is less than 5mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.
REFERENCES
- Aslan G., Cakir Z., Ergintav S., Lasserre C., & Renard F. (2018). Analysis of secular ground motions in Istanbul from a long‐term InSAR time‐series (1992–2017). Remote Sensing, 10(3), 408.Google Scholar
- Azimbeik K., Hossein Mahdavi S., & Rahimzadeh Rofooei F. (2023). Improved image‐based, full‐field structural displacement measurement using template matching and camera calibration methods. Measurement: Journal of the International Measurement Confederation, 211,
112650 .Google Scholar - Bay H., Ess A., Tuytelaars T., & Van Gool L. (2008). Speeded‐up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.Google ScholarDigital Library
- Beauchemin S. S., & Barron J. L. (1995). The computation of optical flow. ACM Computing Surveys (CSUR), 27(3), 433–466.Google ScholarDigital Library
- Benoit L., Briole P., Martin O., Thom C., Malet J. P., & Ulrich P. (2015). Monitoring landslide displacements with the Geocube wireless network of low‐cost GPS. Engineering Geology, 195, 111–121.Google Scholar
- Briechle K., & Hanebeck U. D. (2001).
Template matching using fast normalized cross correlation . In Casasent D. P. & Chao T.‐H. (Eds.), Optical pattern recognition XII (Vol. 4387, pp. 95–102). SPIE.Google ScholarCross Ref - Chen J. G., Wadhwa N., Cha Y. J., Durand F., Freeman W. T., & Buyukozturk O. (2015). Modal identification of simple structures with high‐speed video using motion magnification. Journal of Sound and Vibration, 345, 58–71.Google ScholarCross Ref
- Chen L. C., Zhu Y., Papandreou G., Schroff F., & Adam H. (2018). Encoder‐decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany (pp. 801–808).Google Scholar
- Dong C. Z., Ye X. W., & Jin T. (2018). Identification of structural dynamic characteristics based on machine vision technology. Measurement: Journal of the International Measurement Confederation, 126, 405–416.Google Scholar
- Dong C. Z., Celik O., Catbas F. N., O'Brien E. J., & Taylor S. (2020). Structural displacement monitoring using deep learning‐based full field optical flow methods. Structure and Infrastructure Engineering, 16(1), 51–71.Google Scholar
- Garrido‐Jurado S., Muñoz‐Salinas R., Madrid‐Cuevas F. J., & Marín‐Jiménez M. J. (2014). Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognition, 47(6), 2280–2292.Google ScholarDigital Library
- Geiger A., Moosmann F., Car Ö., & Schuster B. (2012). Automatic camera and range sensor calibration using a single shot. 2012 IEEE International Conference on Robotics and Automation, St Paul, MN (pp. 3936–3943).Google Scholar
- Girshick R., Donahue J., Darrell T., & Malik J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH (pp. 580–587).Google Scholar
- Jeong J. H., & Jo H. (2022). Real‐time generic target tracking for structural displacement monitoring under environmental uncertainties via deep learning. Structural Control and Health Monitoring, 29(3), e2902.Google Scholar
- Li S., Liang Z., & Guo P. (2023). A FBG pull‐wire vertical displacement sensor for health monitoring of medium‐small span bridges. Measurement, 211,
112613 .Google Scholar - Li Y., Huang J., Jiang S. H., Huang F., & Chang Z. (2017). A web‐based GPS system for displacement monitoring and failure mechanism analysis of reservoir landslide. Scientific Reports, 7(1), 1–13.Google Scholar
- Lu B., Bai B., & Zhao X. (2023). Vision‐based structural displacement measurement under ambient‐light changes via deep learning and digital image processing. Measurement: Journal of the International Measurement Confederation, 208, 112480. https://doi.org/10.1016/j.measurement.2023.112480Google Scholar
- Luo L., & Feng M. Q. (2018). Edge‐enhanced matching for gradient‐based computer vision displacement measurement. Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1019–1040.Google ScholarDigital Library
- Lv J., Hu Z., Ren G., Zhang C., & Liu Y. (2019). Research on new FBG displacement sensor and its application in Beijing Daxing airport project. Optik, 178, 146–155.Google Scholar
- Ma Z., Choi J., & Sohn H. (2022). Real‐time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements. Computer‐Aided Civil and Infrastructure Engineering, 37(6), 688–703.Google ScholarDigital Library
- Ma Z., Choi J., & Sohn H. (2023). Three‐dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi‐rate Kalman filter. Engineering Structures, 292,
116535 .Google Scholar - Ngeljaratan L., Moustafa M. A., & Pekcan G. (2021). A compressive sensing method for processing and improving vision‐based target‐tracking signals for structural health monitoring. Computer‐Aided Civil and Infrastructure Engineering, 36(9), 1203–1223.Google ScholarDigital Library
- Redmon J., Divvala S., Girshick R., & Farhadi A. (2016). You only look once: Unified, real‐time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016, Las Vegas, NV (pp. 779–788).Google Scholar
- Ronneberger O., Fischer P., & Brox T. (2015). U‐Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer‐Assisted Intervention—MICCAI 2015, Munich, Germany (pp. 234–241).Google Scholar
- Rublee E., Rabaud V., Konolige K., & Bradski G. (2011). ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision, Barcelona, Spain (pp. 2564–2571).Google Scholar
- Schweitzer H., Bell J. W., & Wu F. (2002). Very fast template matching. Computer Vision—ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark (pp. 358–372).Google Scholar
- Shaik S. A., Hoang T., Mechitov K., & Spencer B. F. (2022). Wireless SmartVision system for synchronized displacement monitoring of railroad bridges. Computer‐Aided Civil and Infrastructure Engineering, 37(9), 1070–1088.Google Scholar
- Shao Y., Li L., Li J., An S., & Hao H. (2021). Computer vision based target‐free 3D vibration displacement measurement of structures. Engineering Structures, 246,
113040 . https://doi.org/10.1016/j.engstruct.2021.113040Google ScholarCross Ref - Song Q., Wu J., Wang H., An Y., & Tang G. (2022). Computer vision‐based illumination‐robust and multi‐point simultaneous structural displacement measuring method. Mechanical Systems and Signal Processing, 170,
108822 .Google Scholar - Sun C., Gu D., & Lu X. (2023). Three‐dimensional structural displacement measurement using monocular vision and deep learning based pose estimation. Mechanical Systems and Signal Processing, 190,
110141 .Google ScholarCross Ref - Sun Q., Zhang L., Ding X. L., Hu J., Li Z. W., & Zhu J. J. (2015). Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sensing of Environment, 156, 45–57.Google ScholarCross Ref
- Tsai Z. X., You G. J. Y., Lee H. Y., & Chiu Y. J. (2012). Use of a total station to monitor post‐failure sediment yields in landslide sites of the Shihmen reservoir watershed, Taiwan. Geomorphology, 139‐140, 438–451.Google Scholar
- Wang J., Gao J., Liu C., & Wang J. (2010). High precision slope deformation monitoring model based on the GPS/Pseudolites technology in open‐pit mine. Mining Science and Technology, 20(1), 126–132.Google Scholar
- Wang M., Ao W. K., Bownjohn J., & Xu F. (2022). A novel gradient‐based matching via voting technique for vision‐based structural displacement measurement. Mechanical Systems and Signal Processing, 171,
108951 .Google Scholar - Wu H., Guo Y., Xiong L., Liu W., Li G., & Zhou X. (2019). Optical fiber‐based sensing, measuring, and implementation methods for slope deformation monitoring: A review. IEEE Sensors Journal, 19(8), 2786–2800.Google Scholar
- Wu T., Tang L., Shao S., Zhang X., Liu Y., Zhou Z., & Qi X. (2022). Accurate structural displacement monitoring by data fusion of a consumer‐grade camera and accelerometers. Engineering Structures, 262,
114303 .Google Scholar - Xu H., Zhang J., Cai J., Rezatofighi H., & Tao D. (2022). GMFlow: Learning optical flow via global matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA (pp. 8121–8130).Google Scholar
- Yin Y., Yu Q., Hu B., Zhang Y., Chen W., Liu X., & Ding X. (2023). A vision monitoring system for multipoint deflection of large‐span bridge based on camera networking. Computer‐Aided Civil and Infrastructure Engineering, 38(13), 1879–1891.Google ScholarDigital Library
- Yu S., Zhang J., & He X. (2020). An advanced vision‐based deformation measurement method and application on a long‐span cable‐stayed bridge. Measurement Science and Technology, 31(6),
065201 .Google Scholar - Zhang Z., & Member S. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334.Google ScholarDigital Library
- Zhao S., Kang F., & Li J. (2018). Displacement monitoring for slope stability evaluation based on binocular vision systems. Optik, 171, 658–671.Google Scholar
- Zhu J., Lu Z., & Zhang C. (2021). A marker‐free method for structural dynamic displacement measurement based on optical flow. Structure and Infrastructure Engineering, 18(1), 84–96.Google Scholar
Cited By
View all
Recommendations
- Multi-camera stereo vision based on weights
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
The improvement of measurement accuracy has always been a hot topic in visual measurement. The multi-camera stereo vision, which is composed of more than two cameras, provides more image information, stronger interference capability and higher 3D ...
Read More
- Camera Calibration via Stereo Vision Using Tsai's Method
ETCS '09: Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 03
To enable a mobile robot to navigate in an unknown environment, vision is used as a primary navigation tool. The paper focuses on studying camera calibration via stereo vision. Tsai’s camera calibration technique will be introduced and tested with our ...
Read More
- Stereo vision using two PTZ cameras
The research of traditional stereo vision is mainly based on static cameras. As PTZ (Pan-Tilt-Zoom) cameras are able to obtain multi-view-angle and multi-resolution information, they have received more and more concern in both research and real ...
Read More
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Article
- Information
- Contributors
Published in
Computer-Aided Civil and Infrastructure Engineering Volume 39, Issue 13
1 July 2024
163 pages
ISSN:1093-9687
EISSN:1467-8667
DOI:10.1111/mice.v39.13
Issue’s Table of Contents
© 2024 The Authors. Computer‐Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor.
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Sponsors
In-Cooperation
Publisher
John Wiley & Sons, Inc.
United States
Publication History
- Published: 23 February 2024
Qualifiers
- research-article
Conference
Funding Sources
Other Metrics
View Article Metrics
- Bibliometrics
- Citations0
Article Metrics
- View Citations
Total Citations
Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
View Author Metrics
Cited By
This publication has not been cited yet
Digital Edition
View this article in digital edition.
View Digital Edition
- Figures
- Other
Close Figure Viewer
Browse AllReturn
Caption
View Issue’s Table of Contents