Quantification of phase-based magnified motion using image enhancement and optical flow techniques

Valente, Nicholas A.; Cabo, Celso T. do; Mao, Zhu; Niezrecki, Christopher

doi:10.1016/j.measurement.2021.110508

Cited by 27 publications

(1 citation statement)

References 34 publications

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“…In addition, researchers have developed various phase-based algorithms for structural displacement measurement. Chen et al developed a phase-based motion magnifcation algorithm for structural displacement measurement and modal identifcation of simple structures [26]; Cha et al developed a phase-based optical fow method for structural displacement measurement and bolt loosening detection using the unscented Kalman flters [27]; Valente et al quantifed the amount of physical motion with the degree of magnifcation in phase-based displacement extraction method [28]; Shao et al developed a target-free three-dimensional (3D) tiny displacement measurement method by deep learning and motion magnifcation technique [29,30]; Luo et al proposed a broadband phase-based motion magnifcation and line tracking algorithm for cable displacement and cable tension force estimation [31]. Although the computer vision-based method has been widely investigated for 2D and 3D displacement measurement of civil infrastructure, there are two challenging problems that need to be addressed when applying this method to complex construction sites.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Pier Settlement Displacement Measurement of a Multispan Continuous Concrete Highway Bridge under Complex Construction Environments

Zhan,

Huang,

Fan

et al. 2024

Structural Control and Health Monitoring

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Various concrete bridges have been built across oceans, valleys, and mountains; however, the settlement displacement of bridge piers caused by environmental changes or self-weight during construction phases often leads to uneven stresses, cracking, and eventual collapse. To address the labor-intensive and high-cost issues of pier displacement monitoring using contact-type sensors, this paper proposes an automatic vision-based method for measuring pier settlement displacement under complex construction environments, such as complex image backgrounds, varying ambient light, and camera movement. In the proposed method, a deep learning network was first employed to eliminate the adverse effect of complex image backgrounds and varying ambient light on the accuracy of target detection; then, an adaptive displacement extraction algorithm without a human-computer interaction process was developed to automatically extract the center coordinates of targets attaching to the bridge piers and reference platform; finally, the pier settlement displacement was calculated by using the relative displacements obtained by a dual camera system to eliminate the measurement error caused by camera translation and rotation movements. Laboratory tests of a cantilever beam and field tests of a continuous multispan concrete girder highway bridge under construction have successfully validated the effectiveness and robustness of the developed methodology. The results obtained in this paper can provide some insights for engineers in applying computer vision technology for the real-time monitoring of bridge displacements.

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