Deflection Measurement of Bridge Using Images Captured Under the Bridge by Sampling Moiré Method

Ri, Shien; Wang, Qinghua; Tsuda, Hiroshi; Shirasaki, H.; Kuribayashi, Kenichi

doi:10.1007/s40799-022-00616-y

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…3. Notably, the SM method has been successfully used in previous studies to measure deflections of bridges using a fixed camera, achieving ultrahigh precision on the sub-millimeter accuracy [45][46][47] . In the present study, we adopt the SM method to extract accurate marker center coordinates, which not only compensates for image blur induced by camera motion but also facilitates highly accurate estimation of deflection.…”

Section: Drone-based Displacement Measurement Approachmentioning

confidence: 99%

Drone-based displacement measurement of infrastructures utilizing phase information

Ri,

Ye,

Toyama

et al. 2024

Nat Commun

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Drone-based inspections provide an efficient and flexible approach to assessing aging infrastructures while prioritizing safety. Here, we present a pioneering framework that employs drone cameras for high-precision displacement measurement and achieves sub-millimeter accuracy, meeting the requirements for on-site inspections. Inspired by the principles of human auditory equilibrium, we have developed an effective scheme using a group of strategical reference markers on the bridge girders to measure structural displacements in the bridge. Our approach integrates the phase-based sampling moiré technique with four degrees-of-freedom geometric modeling to accurately delineate the desired bridge displacements from camera motion-induced displacements. The proposed scheme demonstrates favorable precision with accuracy reaching up to 1/100th of a pixel. Real-world validations further confirmed the reliability and efficiency of this technique, making it a practical tool for bridge displacement measurement. Beyond its current applications, this methodology holds promise as a foundational element in shaping the landscape of future autonomous infrastructure inspection systems.

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Section: Drone-based Displacement Measurement Approachmentioning

confidence: 99%

Drone-based displacement measurement of infrastructures utilizing phase information

Ri,

Ye,

Toyama

et al. 2024

Nat Commun

Self Cite

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“…The analysis revealed that several sensors had been used for validation purposes, including laser displacement sensors [32,34,41,42,49,70,74,76,82], accelerometers [32][33][34]50,51,53,55,59,61,[64][65][66][67][68]76], IMU [60], linear variable differential transformers [34,36,41,50,54,61,66,67,73], laser Doppler vibrometers [38,46], stationary cameras [42,75,83], actuators [71], laser trackers [81], infrared sensors [48], and marker-based systems [44,73]. Marker-based systems are routinely used in other fields (such as medicine), where they are the traditional reference systems for motion capture and analysis.…”

Section: Validation With Gold-standardmentioning

confidence: 99%

“…In contrast, the authors of [39,46,47,52,56,68,79,[83][84][85] tested the proposed solution only directly on real scenarios. The authors of [48] also mentioned an in-field experiment (i.e., estimation of building vibration using a video recorded from the roof of a building).…”

Section: Experimental and Real-world Scenariosmentioning

confidence: 99%

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Ferraris

Amprimo

Pettiti

2023

Signals

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Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

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Analysis of precursor response characteristics and time-frequency vibration parameters of rock failure: a laboratory experimental method

Dong,

Qiao,

et al. 2024

Nondestructive Testing and Evaluation

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Deflection Measurement of Bridge Using Images Captured Under the Bridge by Sampling Moiré Method

Cited by 8 publications

References 36 publications

Drone-based displacement measurement of infrastructures utilizing phase information

Drone-based displacement measurement of infrastructures utilizing phase information

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Analysis of precursor response characteristics and time-frequency vibration parameters of rock failure: a laboratory experimental method

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