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基于条纹投影和散斑相关的高精度三维变形测量系统。

High-Accuracy Three-Dimensional Deformation Measurement System Based on Fringe Projection and Speckle Correlation.

机构信息

School of Science, Nanjing University of Science and Technology, Nanjing 210094, China.

出版信息

Sensors (Basel). 2023 Jan 6;23(2):680. doi: 10.3390/s23020680.

DOI:10.3390/s23020680
PMID:36679475
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9866896/
Abstract

Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.

摘要

结构光三维轮廓术（FPP）和数字图像相关（DIC）广泛应用于三维（3D）测量中。DIC 和 FPP 的结合可以有效地克服各自的缺点。然而，物体表面的散斑严重影响相机拍摄的条纹图像的质量和调制，这将导致测量结果产生不可忽视的误差。在本文中，我们提出了一种基于深度学习技术的条纹图像提取方法，该方法可以将嵌入散斑的条纹图像转换为无散斑的条纹图像。介绍了该方法的原理、3D 坐标计算和变形测量。与传统的 3D-DIC 方法相比，实验结果表明该方法是有效和精确的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/d9f28699bce4/sensors-23-00680-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/c66ffff6b9a7/sensors-23-00680-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/d9b437c31145/sensors-23-00680-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/42c55d2d144f/sensors-23-00680-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/0fc3c7ad4227/sensors-23-00680-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/474986dcda4c/sensors-23-00680-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/1f2e6c1301a3/sensors-23-00680-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/f99320b33077/sensors-23-00680-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/997bb9a9995e/sensors-23-00680-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/df523fee1497/sensors-23-00680-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/589efe2182f3/sensors-23-00680-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/7c6571fe2436/sensors-23-00680-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/999b03e7b42e/sensors-23-00680-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/d9f28699bce4/sensors-23-00680-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/c66ffff6b9a7/sensors-23-00680-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/d9b437c31145/sensors-23-00680-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/42c55d2d144f/sensors-23-00680-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/0fc3c7ad4227/sensors-23-00680-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/474986dcda4c/sensors-23-00680-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/1f2e6c1301a3/sensors-23-00680-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/f99320b33077/sensors-23-00680-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/997bb9a9995e/sensors-23-00680-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/df523fee1497/sensors-23-00680-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/589efe2182f3/sensors-23-00680-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/7c6571fe2436/sensors-23-00680-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/999b03e7b42e/sensors-23-00680-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d4d/9866896/d9f28699bce4/sensors-23-00680-g013.jpg

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J Mol Cell Cardiol. 2022 May;166:74-90. doi: 10.1016/j.yjmcc.2022.02.004. Epub 2022 Feb 25.
2
Deep learning-based fringe modulation-enhancing method for accurate fringe projection profilometry.基于深度学习的条纹调制增强方法用于精确条纹投影轮廓术
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3
Adaptive Binocular Fringe Dynamic Projection Method for High Dynamic Range Measurement.
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Sensors (Basel). 2019 Sep 18;19(18):4023. doi: 10.3390/s19184023.
4
Phase-shifting profilometry combined with Gray-code patterns projection: unwrapping error removal by an adaptive median filter.结合格雷码图案投影的相移轮廓术：通过自适应中值滤波器去除展开误差
Opt Express. 2017 Mar 6;25(5):4700-4713. doi: 10.1364/OE.25.004700.
5
High-speed three-dimensional profilometry for multiple objects with complex shapes.用于多个复杂形状物体的高速三维轮廓测量法。
Opt Express. 2012 Aug 13;20(17):19493-510. doi: 10.1364/OE.20.019493.
6
High-resolution, real-time 3D absolute coordinate measurement based on a phase-shifting method.基于相移法的高分辨率实时三维绝对坐标测量
Opt Express. 2006 Apr 3;14(7):2644-9. doi: 10.1364/oe.14.002644.