Ren Xiangyun, Xu Xiangyang, Yuan Fang, Yin Zhuoyi, He Xiaoyuan
Appl Opt. 2022 Aug 20;61(24):7181-7188. doi: 10.1364/AO.465070.
Digital image correlation (DIC) has been widely used in both experimental mechanics and engineering fields. The matching algorithm of the DIC method usually requires surfaces containing a random speckle pattern as a deformation information carrier. The speckle pattern plays an irreplaceable role in DIC, which has led to extensive research on it. However, most previous research had always focused on the fabrication and computational performance of the speckle, ignoring the value of intentionally defining the meaning of speckle in design. In this study, we describe a novel, to the best of our knowledge, speckle pattern named semantic speckle. It is a digital speckle composed of several different speckle patterns with similar characteristics. Based on the deep-learning method and matching algorithm, the central location of the semantic part in the overall speckle image can be obtained automatically. Through the intentional definition of the semantic part, it can be possible to calibrate the camera parameters and correct the external parameters of the DIC systems.
数字图像相关（DIC）已在实验力学和工程领域中广泛应用。DIC方法的匹配算法通常需要包含随机散斑图案的表面作为变形信息载体。散斑图案在DIC中起着不可替代的作用，这引发了对其的广泛研究。然而，以往的大多数研究一直集中在散斑的制作和计算性能上，而忽略了在设计中有意定义散斑含义的价值。在本研究中，据我们所知，我们描述了一种名为语义散斑的新型散斑图案。它是由几个具有相似特征的不同散斑图案组成的数字散斑。基于深度学习方法和匹配算法，可以自动获得语义部分在整体散斑图像中的中心位置。通过有意定义语义部分，可以校准相机参数并校正DIC系统的外部参数。
