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A structural 3D displacement measurement method using monocular camera based on multiple feature points tracking
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DOI：10.1016/j.measurement.2024.116406.png)
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摘要
En 中文
Structural displacement measurement techniques based on monocular vision have made great strides in the past decades, however, practical applications still face challenges. These challenges include the complexity and cost associated with long-time automatic monitoring, as well as limitations in dynamic displacement tracking. This study introduces an automatic visual method for measuring three-dimensional (3D) displacement using the monocular camera. The method leverages an improved feature points group tracking algorithm designed for long-time image sequences to achieve accurate measurements of both planar and 3D displacements. This technology addresses challenges associated with error accumulation due to extended time series and small sub-pixel vibrations in the tracking algorithm. In addition, the depth estimation of monocular vision is improved, thus improved the accuracy of displacement measurements. To validate the accuracy of the proposed method in displacement measurement, model experiments were conducted on a three-story frame model. In addition, a cable-stayed bridge model was tested to assess the robustness of the method for depth estimation. The results show that the proposed method achieves good results in different scenarios and is able to realize 3D spatial displacement measurement, and the measurement results are basically consistent with the actual values.
Keyword:
Monocular camera
Deep learning
3D displacement measurement
Depth estimation
Multiple feature points tracking
期刊
IF:
5.6
论文数:
2.1W
被引数:
5.4W
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