Full-Field Bridge Deflection Monitoring with Off-Axis Digital Image Correlation

The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.

Section: Spectrum-aware Video Magnificationmentioning

confidence: 99%

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

Cai

Lin

et al. 2022

“…Researchers have developed various non-contact measurement techniques to address the limitations of existing contact sensors, such as GPS [1][2][3][4], laser Doppler velocimeters (LDVs) [5][6][7], radar interferometers [8][9][10][11], and robots or unmanned aerial vehicles (UAVs) [12][13][14]. Among these, vision sensor-based methods have received attention from many researchers because they are economical, enable remote simultaneous multi-point measurement, and are easy to install [15][16][17][18][19][20][21]. Pan et al used these and the digital image correlation method to measure bridge displacement [19].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, researchers applied the vision sensor concept using LEDs as the active markers. Using this method, Tian and Pan developed a vision sensor-based bridge deflection measurement system and monitored a bridge to verify the system [20,21]. Although vision sensors have numerous advantages, it is difficult to measure as precisely as contact or other non-contact sensors.…”

Section: Introductionmentioning

confidence: 99%

Development of a Remote Displacement Measuring Laser System for Bridge Inspection

Kim

Jung

2022

Measuring displacement is essential for assessing the safety of bridges. Non-contact sensors such as vision sensors can precisely measure displacement but may be expensive or incapable of micro-scale measurement at a low cost, unlike contact displacement sensors, which are economical but challenging to install. This study proposes an economical, remote non-contact sensor system. The system comprises a laser beam transmitter and a light receiver, deriving the displacement based on the position where the laser beam is irradiated to the light-receiving surface. To measure this, the light receiver was installed at the measurement point and included a wireless communicator to transmit the displacement data. A displacement experiment was conducted to evaluate the performance. The results confirmed that precise displacement measurements were possible at a resolution of 100 µm. For bridge load tests, a light receiver under a bridge was installed, laser beams irradiated to the light-receiving surface from a distance, and the displacement was measured for each test and compared with the values measured by a conventional contact sensor. The results were highly consistent with those of the existing sensor, indicating that the proposed sensor system applies to bridge loading tests and the safety diagnosis for various structures.

“…In addition, DIC is more robust to brightness variations because the method computes the correlations between regions instead of estimating through pixel intensity. In applications, DICs are usually combined with speckle pattern coatings attached to the structural surface, and accurate full-field displacements are then obtained [ 13 , 14 , 15 ]. The vibrational measurements obtained from DIC can also be used for modal property extraction [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study

Chou

Chang

2021

Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due to lack of reference points and structural scales and complexity. Alternatively, structural displacements can be acquired using computer vision-based motion extraction techniques. These extracted motions not only provide vibrational responses but are also useful for identifying the modal properties. In this study, three methods, including the optical flow with the Lucas–Kanade method, the digital image correlation (DIC) with bilinear interpolation, and the in-plane phase-based motion magnification using the Riesz pyramid, are introduced and experimentally verified using a four-story steel-frame building with a commercially available camera. First, the three displacement acquiring methods are introduced in detail. Next, the displacements are experimentally obtained from these methods and compared to those sensed from linear variable displacement transducers. Moreover, these displacement responses are converted into modal properties by system identification. As seen in the experimental results, the DIC method has the lowest average root mean squared error (RMSE) of 1.2371 mm among these three methods. Although the phase-based motion magnification method has a larger RMSE of 1.4132 mm due to variations in edge detection, this method is capable of providing full-field mode shapes over the building.