数字散斑相关方法(DSCM)是一种可以测量变形和应变的光学非接触测量方法, 其通过对变形前后物体表面的图像进行灰度信息相关计算来获取被测物的力学性能。本文叙述了数字散斑相关方法近年来在国内外的发展动态和应用现状, 详细论述了基于自适应遗传算法、智能神经网络方法、小波变换法的一系列新型相关搜索方法。文章指出, 近年来, 数字散斑相关技术已发展到相对成熟, 目前的研究重点是提高测试精度和图像处理速度, 而提高散斑图像质量和研究高效的算法是需要努力的方向。

Digital speckle correlation(DSCM) is a noncontact measuring method for displacements and strains, which obtains the mechanical properties of an object by calculating the gray information correlation of the object images before and after deformations. The method has been applied successfully in mechanical measurements in the past twenty years. This paper introduces the developing states of the DSCM and gives application examples. Some new technologies involved in the DSCM are reviewed, such as genetic algorithm, neural networks and wavelet transform. Finally, it points out that DSCM research will focus on improving measuring accuracy and image processing speeds in the future, including improving speckle image quality and researching higher effective algorithms.