Bridge vibration measurements using different camera placements and techniques of computer vision and deep learning
Open accessAbstract
In this paper, a new framework is proposed for monitoring the dynamic performance of bridges using three different camera placements and a few visual data processing techniques at low cost and high efficiency. A deep learning method validated by an optical flow approach for motion tracking is included in the framework. To verify it, videos taken by stationary cameras of two shaking table tests were processed at first. Then, the vibrations of six pedestrian bridges were measured using structure-mounted, remote, and drone-mounted cameras, respectively. Two techniques, displacement and frequency subtractions, are applied to remove systematic motions of cameras and to capture the natural frequencies of the tested structures. Measurements on these bridges were compared with the data from wireless accelerometers and structural analysis. Influences of critical parameters for camera setting and data processing, such as video frame rates, data window size, and data sampling rates, were also studied carefully. The research results show that the vibrations and frequencies of structures on the shaking tables and existing bridges can be captured accurately with the proposed framework. These camera placements and data processing techniques can be successfully used for monitoring their dynamic performance.
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The frequency less than 1.0 Hz was cut off when the vibration measurements by remote cameras were filtered and denoised.
“But it should be noticed that the frequency less than 1.0 Hz was cut off when the vibration measurements by remote cameras were filtered and denoised [ 9 ].”
From Field monitoring and assessment of reinforced concrete building collapse using remote sensing and digital twins (2026) · cites this paper as [9]
Section 3.4 says the camera-measured vibration signals were filtered with a Butterworth band-pass filter to suppress very low and high frequencies. It does not specify a 1.0 Hz cutoff, so the precise threshold attributed to the paper is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe 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 [9]
Section 4.2.1 reports that both Mask R-CNN + SIFT and the LK tracker were used to measure vibrations on an in-service bridge. Sections 3.2 and 6 report successful laboratory measurements of concrete-beam deflection with Mask R-CNN + SIFT, citing an earlier study, but do not clearly establish that the LK tracker also captured the concrete-beam displacements. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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Research works printed but not linked: 1
- Bai Y (2022) Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation
Cited by
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Assessment and monitoring of bridges using various camera placements and structural analysis
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Self-citationBai, Y., Sezen, H., Yilmaz, A., & Qin, R. (2026) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-01044-3
Self-citation
- Authors
- Bai, Y., Sezen, H., Yilmaz, A., & Qin, R.
- Affiliations
- The Ohio State University (US)
- Corresponding author
- Sezen, Halil
- Venue
- Advances in Bridge Engineering
- Published
- 2023-11-01
- Volume
- 4
- Issue
- 1
- ISSN
- 2662-5407 (ISSN-L)
- OpenAlex
- W4388036548
- Cited by
- 19
- References
- 30
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 4 · 2023: 2
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Advances in Bridge Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Directorate for Engineering (2036193)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- bridge vibration monitoring, camera placement, drone-mounted cameras, deep learning, shaking table test, pedestrian bridge, natural frequency measurement, video frame rate, sampling frequency
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