Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method
Open accessAbstract
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments. The latest Mask Regional Convolutional Neural Network (Mask R-CNN) can locate the targets and monitor their movements from videos recorded by a stationary camera. To improve precision and remove the noise, techniques such as Scale-invariant Feature Transform (SIFT) and various filters for signal processing are included. Experiments on three small-scale reinforced concrete beams and a shaking table test are utilized to verify the proposed method. Results show that the proposed deep learning method can achieve the goal to automatically and precisely measure the motion of tested structural members during laboratory experiments.
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The Mask R-CNN + SIFT and LK tracker methods successfully captured displacements of concrete beams in the laboratory and vibrations of in-service bridges.
“The Mask R-CNN + SIFT and LK tracker methods successfully captured the displacements of concrete beams in the laboratory and the vibrations of in-service bridges [8 -10].”
From Field monitoring and assessment of reinforced concrete building collapse using remote sensing and digital twins (2026) · cites this paper as [8]
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Self-citationLi, M., Wang, S., Fu, T., Lin, S., & Sun, R. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3600728 · Cited by 3 · see citing works
Bai, Y., Sezen, H., Yilmaz, A., & Qin, R. (2026) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-01044-3
Self-citationTopoRelTrack: A Self-Supervised Visual Tracking Method for Video-Based Seismic Motion Measurement
Wang, H., Su, J., Li, J., Niu, S., Fan, B., & Luo, B. (2026) · Applied Sciences · doi:10.3390/app162110459
- Authors
- Bai, Y., Abduallah, R. M., Sezen, H., & Yilmaz, A.
- Affiliations
- The Ohio State University (US)
- ORCID
- Sezen, Halil
- Venue
- 2021 IEEE Sensors
- Publisher
- IEEE
- Published
- 2021-10-31
- Pages
- 1-4
- ISSN
- 1930-0395 (ISSN-L)
- OpenAlex
- W3200220620
- Cited by
- 5
- References
- 23
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2
- FWCI
- 2.15
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- displacement measurement, vibration measurement, structural testing, Mask R-CNN, video-based motion tracking, SIFT feature matching, reinforced concrete beams, shaking table test, structural health monitoring, automated data collection
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