Semantic speckle: an auto-located speckle pattern for DIC measurement

Abstract

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.