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研究生: 任志振
Ren, Zhi-Zhen
論文名稱: 三維數位影像相關法變形量測研發及應用
Development and Application of Three-Dimensional Digital Image Correlation for Deformation Measurements
指導教授: 陳元方
Chen, Yuan-Fang
學位類別: 碩士
Master
系所名稱: 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年: 2026
畢業學年度: 114
語文別: 中文
論文頁數: 109
中文關鍵詞: 相機校正三維數位影像相關法懸臂樑
外文關鍵詞: camera calibration, three-dimensional digital image correlation, cantilever beam
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  • 本研究旨在開發一套完整的 3D-DIC 變形量測系統,並驗證其量測準確度。系統實作涵蓋雙相機校正、立體匹配、三維重建以及應變場計算等完整流程。雙相機校正部分使用棋盤格校正板,透過雙相機系統獲取相機的內部參數(焦距、影像中心與透鏡畸變係數)與外部參數(兩相機之間的相對旋轉與平移關係)。匹配演算法以零均值正規化平方差和(Zero-mean Normalized Sum of Squared Difference, ZNSSD)作為相關性準則,並採用 Newton-Raphson Method迭代法和雙立方樣條內插法達到次像素級的匹配精度。三維重建則藉由已知的相機內外參數與匹配所得的影像對應點,先以線性方程式Ax=b求得初始解,再透過 Levenberg-Marquardt 非線性最佳化演算法,以最小化重投影誤差求解出最佳的三維座標。應變場計算採用逐點最小二乘法(Pointwise Least Squares, PLS),透過對每個追蹤點周圍進行多項式擬合,可以從擬合出的多項式係數得到位移梯度,再代入Lagrangian 應變公式,求得應變。
    本研究進行了六組位移實驗以驗證本系統的量測準確度,將試件沿 X、Y、Z 三個方向同時平移相同量,分別為0.1mm、0.2mm、0.5mm、1 mm、2 mm 與 3 mm。首先比較三種不同相關性準則平均誤差,接著和商業軟體 VIC-3D 比較六組的位移實驗。最後實驗驗證為懸臂樑彎曲實驗,量測樑的撓度,並與 VIC-3D 軟體的量測結果進行比較。在位移實驗中,三種相關性準則的比較結果顯示,在 X 方向,ZNSSD 的平均誤差為 0.001~0.002 mm,遠小於 SSD 與 NSSD 的 0.02~0.05 mm;在 Y 方向,ZNSSD 的平均誤差為 0.002~0.012 mm,亦小於 SSD 與 NSSD 的 0.01~0.03 mm;在 Z 方向,ZNSSD 的平均誤差為 0.014~0.047 mm,同樣優於 SSD 與 NSSD 的 0.02~0.08 mm。此結果驗證了 ZNSSD 優於SSD 與 NSSD。進一步將本系統與商業軟體 VIC-3D 進行比較,在 X 方向,本系統的平均誤差為 0.001~0.002 mm,優於 VIC-3D 的 0.007~0.025 mm;在 Y 方向,本系統的平均誤差為 0.002~0.012 mm,相較於 VIC-3D 的 0.002~0.003 mm 略高約 0.009 mm,但相對誤差仍小於 0.4%;在 Z 方向,本系統的平均誤差為 0.014~0.047 mm,相較於 VIC-3D 的 0.001~0.018 mm 略高約 0.029 mm,相對誤差仍小於 1.4%。整體而言,本系統在 X 方向的位移量測表現最佳,在 Y 與 Z 方向的量測精度雖略低於 VIC-3D,但相對誤差皆控制在 2% 以內,證實本系統具有良好的量測準確度。在懸臂樑彎曲實驗中,將本系統與 VIC-3D 的位移結果進行比較,以 VIC-3D 的數據作為標準值、本系統的數據作為實際值,針對126個位移點進行誤差分析。結果顯示平均誤差為 0.007 mm,標準差為0.007mm,最大誤差為 0.012 mm。

    This study aims to develop a complete 3D-DIC deformation measurement system and to validate its measurement accuracy. The system implementation covers the entire process, including stereo camera calibration, stereo matching, three-dimensional reconstruction, and strain field computation. For stereo camera cali-bration, a checkerboard calibration board was used to obtain, through the stereo camera system, the intrinsic parameters (focal length, principal point, and lens distortion coefficients) and the extrinsic parameters (the relative rotation and translation between the two cameras). In the matching algorithm, the zero-mean normalized sum of squared difference (ZNSSD) was adopted as the correlation criterion, and the Newton-Raphson iterative method combined with bicubic spline interpolation was employed to achieve sub-pixel matching accuracy. For three-dimensional reconstruction, based on the known intrinsic and extrinsic camera parameters and the corresponding image points obtained from matching, an initial solution was first computed using the linear equation Ax = b, and the optimal three-dimensional coordinates were then obtained by minimizing the reprojection error through the Levenberg-Marquardt nonlinear optimization algorithm. For strain field computation, the pointwise least squares (PLS) method was employed; by performing polynomial fitting around each tracking point, the displacement gradients were obtained from the fitted polynomial coefficients and substituted into the Lagrangian strain formula to determine the strain.
    Six sets of displacement experiments were conducted to validate the measurement accuracy of the developed system, in which the specimen was translated simulta-neously along the X, Y, and Z directions by the same amount of 0.1, 0.2, 0.5, 1, 2, and 3 mm, respectively. First, the mean errors of three different correlation crite-ria were compared; the six sets of displacement experiments were then compared with the commercial software VIC-3D. Finally, a cantilever beam bending exper-iment was performed to measure the deflection of the beam, and the results were compared with those measured by VIC-3D. In the displacement experiments, the comparison of the three correlation criteria showed that, in the X direction, the mean error of ZNSSD was 0.001–0.002 mm, far smaller than the 0.02–0.05 mm of SSD and NSSD; in the Y direction, the mean error of ZNSSD was 0.002–0.012 mm, also smaller than the 0.01–0.03 mm of SSD and NSSD; and in the Z direc-tion, the mean error of ZNSSD was 0.014–0.047 mm, likewise superior to the 0.02–0.08 mm of SSD and NSSD. These results confirmed that ZNSSD outper-forms SSD and NSSD. The developed system was further compared with the commercial software VIC-3D. In the X direction, the mean error of the developed system was 0.001–0.002 mm, superior to the 0.007–0.025 mm of VIC-3D; in the Y direction, the mean error of the developed system was 0.002–0.012 mm, approxi-mately 0.009 mm higher than the 0.002–0.003 mm of VIC-3D, but with a relative error still below 0.4%; in the Z direction, the mean error of the developed system was 0.014–0.047 mm, approximately 0.029 mm higher than the 0.001–0.018 mm of VIC-3D, with a relative error still below 1.4%. Overall, the developed system performed best in displacement measurement along the X direction; although its measurement accuracy in the Y and Z directions was slightly lower than that of VIC-3D, the relative errors were all controlled within 2%, confirming that the de-veloped system possesses good measurement accuracy. In the cantilever beam bending experiment, the displacement results of the developed system were com-pared with those of VIC-3D, taking the VIC-3D data as the reference values and the developed system data as the measured values; an error analysis was per-formed on 126 displacement points. The results showed a mean error of 0.007 mm, a standard deviation of 0.007 mm, and a maximum error of 0.012 mm.

    摘要 I Abstract III 致謝 X 目錄 XI 表目錄 XIV 圖目錄 XV 第一章 緒論 1 1.1 研究背景 1 1.2 研究目的 2 1.3 文獻回顧 2 1.4 本文架構 5 第二章 二維數位影像相關法 6 2.1 物體變形前後之相關位置 6 2.2 數位影像相關性 8 2.3 尋找最佳參數的方法 10 2.3.1 Coares-Fine Method 10 2.3.2 Newton-Raphson Method 11 2.4 影像雙立方樣條內插法 17 第三章 三維數位影像相關法 19 3.1 立體匹配 19 3.2 雙目視覺原理 20 3.3 兩台相機的相對關係 22 3.4 求解三維空間座標 24 3.5 求解應變 26 第四章 相機校正 28 4.1 相機成像模型 28 4.1.1 透視投影 28 4.1.2 像素非正交性 28 4.1.3 相機成像之座標轉換 29 4.1.4 影像的雙線性內插法 35 4.2 相機校正 36 4.2.1 求解投影轉換矩陣 37 4.2.2 求解內部、外部參數 40 4.2.3 求解畸變參數 43 4.2.4 相機參數的優化 47 第五章 實驗架設與流程 51 5.1 實驗設備和架設以及流程 51 5.1.1 相機規格 51 5.1.2 實驗架設 52 5.1.3 實驗流程 55 5.2 實驗測試 56 5.2.1 不同相關性準則測試 56 5.2.2 三維重建重投影點誤差分析 59 5.2.3 應變計算視窗大小對量測準確度之影響 61 第六章 實驗結果與討論 70 6.1 三軸位移實驗 70 6.2 懸臂樑彎曲實驗 81 第七章 結論與未來展望 86 7.1 結論 86 7.2 未來展望 87 參考文獻 88

    [1].Peters, W. H., & Ranson, W. F. (1982). Digital imaging techniques in experimental stress analysis. Optical Engineering, 21(3), 427-431.
    [2].Sutton, M. A., Wolters, W. J., Peters, W. H., Ranson, W. F., & McNeill, S. R. (1983). Determination of displacements using an improved digital correlation method. Image and Vision Computing, 1(3), 133-139.
    [3].Bruck, H. A., McNeill, S. R., Sutton, M. A., & Peters, W. H. (1989). Digital image cor-relation using Newton-Raphson method of partial differential correction. Experimental Mechanics, 29(3), 261-267.
    [4].Vendroux, G., & Knauss, W. G. (1998). Submicron deformation field measurements: Part 2. Improved digital image correlation. Experimental Mechanics, 38(2), 86-92.
    [5].Luo, P. F., Chao, Y. J., Sutton, M. A., & Peters, W. H. (1993). Accurate measurement of three-dimensional deformations in deformable and rigid bodies using computer vision. Experimental Mechanics, 33(2), 123-132.
    [6].Helm, J. D., McNeill, S. R., & Sutton, M. A. (1996). Improved three-dimensional image correlation for surface displacement measurement. Optical Engineering, 35(7), 1911-1920.
    [7].Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334.
    [8].Hartley, R., & Zisserman, A. (2003). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press.
    [9].Tong, W. (2005). An evaluation of digital image correlation criteria for strain mapping applications. Strain, 41(4), 167-175.
    [10].Sutton, M. A., Turner, J. L., Bruck, H. A., & Chao, T. A. (1991). Full-field representa-tion of discretely sampled surface deformation for displacement and strain analysis. Experimental Mechanics, 31(2), 168-177.
    [11].Wang, C. C. B., Deng, J. M., Ateshian, G. A., & Hung, C. T. (2002). An automated ap-proach for direct measurement of two-dimensional strain distributions within articular cartilage under unconfined compression. Journal of Biomechanical Engineering, 124(5), 557-567.
    [12].Pan, B., Asundi, A., Xie, H., & Gao, J. (2009). Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field meas-urements. Optics and Lasers in Engineering, 47(7), 865-874.
    [13].Blaber, J., Adair, B., & Antoniou, A. (2015). Ncorr: open-source 2D digital image corre-lation MATLAB software. Experimental Mechanics, 55(6), 1105-1122.
    [14].Boukhtache, S., Abdelouahab, K., Berry, F., Blaysat, B., Grédiac, M., & Sur, F. (2021). When Deep Learning Meets Digital Image Correlation. Optics and Lasers in Engineer-ing, 136, 106308.
    [15].張舜雄,“應用數位影像相關法及影像校正於影像拼接之研究”,國立成功大學機械工程研究所碩士論文,2023
    [16].Sutton, M. A., Orteu, J. J., & Schreier, H. W. (2009). Image Correlation for Shape, Mo-tion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer.
    [17].Pan, B., Qian, K., Xie, H., & Asundi, A. (2009). Two-dimensional digital image correla-tion for in-plane displacement and strain measurement: a review. Measurement Science and Technology, 20(6), 062001.
    [18].蘇子勛,“應用數位影像相關法及影像拼接於長工件之量測”,國立成功大學機械工程研究所碩士論文,2024
    [19].Lu, H., & Cary, P. D. (2000). Deformation measurements by digital image correlation: implementation of a second-order displacement gradient. Experimental Mechanics, 40(4), 393-400.
    [20].Schreier, H. W., Braasch, J. R., & Sutton, M. A. (2000). Systematic errors in digital im-age correlation caused by intensity interpolation. Optical Engineering, 39(11), 2915-2921.
    [21].Garcia, D., Orteu, J. J., & Penazzi, L. (2002). A combined temporal tracking and stereo-correlation technique for accurate measurement of 3D displacements: application to sheet metal forming. Journal of Materials Processing Technology, 125-126, 736-742.
    [22].Orteu, J. J. (2009). 3-D computer vision in experimental mechanics. Optics and Lasers in Engineering, 47(3-4), 282-291.
    [23].D. C. Brown. Close-range camera calibration. Photogrammetric Engineering, 37(8):855–866, 1971.
    [24].Tsai, R. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation, 3(4), 323-344.
    [25].Wei, G. Q., & Ma, S. D. (1994). Implicit and explicit camera calibration: theory and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5), 469-480.
    [26].Moré, J. J. (1977). The Levenberg-Marquardt algorithm: implementation and theory. In G. A. Watson (Ed.), Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag.
    [27].https://correlated.kayako.com/article/7-speckle-generator
    [28].Correlated Solutions, Inc., "VIC-3D Digital Image Correlation System,"https://www.correlatedsolutions.com/

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