Liu Gang, Li Mengzhu, Zhang Weiqing, Gu Jiawei
School of Civil Engineering, Chongqing University, No. 83 Shabei Street, Chongqing 400045, China.
Sensors (Basel). 2021 Apr 30;21(9):3140. doi: 10.3390/s21093140.
Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton-Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.
近年来，用于位移和应变测量的数字图像相关（DIC）技术蓬勃发展。在DIC方法中，有整数像素和亚像素匹配步骤来从一系列图像中提取位移，而识别精度主要取决于后者。本研究提出了一种亚像素位移匹配方法，称为基于双精度梯度的算法（DPG）。之后，使用粗-细搜索算法识别整数像素位移。为了提高亚像素提取步骤中的精度和抗噪声能力，采用传统的基于梯度的方法在计算机上分析散斑图案上的数据，并考虑噪声的影响。在一个方向上的这两个最接近的整数像素都被用作插值中心。然后，使用这两个插值中心通过五点双三次样条插值算法提取两个亚像素位移。提出了一种考虑污染噪声的新型组合系数来合并这两个亚像素位移，以获得最终的识别位移。模拟散斑图案和涂漆梁弯曲试验的结果表明，所提方法的精度比传统基于梯度的方法提高了四倍，达到了与牛顿-拉夫逊方法相同的高精度。所提方法的精度有效达到92.67%，高于牛顿-拉夫逊方法，并且具有更好的抗噪声性能和稳定性。
