Three-dimensional structural displacement measurement using monocular vision and deep learning based pose estimation

Sun, Chujin; Gu, Donglian; Lu, Xinzheng

doi:10.1016/j.ymssp.2023.110141

Cited by 29 publications

(1 citation statement)

References 42 publications

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“…Lu et al (2023) proposed a displacement monitoring method that integrates deep learning and traditional methods. C. Sun et al (2023) proposed a 3D displacement estimation method based on deep learning. The great achievements of deep learning algorithms are commendable, but it needs to be confronted that the huge computing power required by deep learning algorithms and the power consumption may be a challenge for field applications.…”

Section: Introductionmentioning

confidence: 99%

A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

Wang,

Peng,

et al. 2024

Computer aided Civil Eng

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Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.

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Section: Introductionmentioning

confidence: 99%