2D full-field deformation measurement at grain level using optical flow with deep networks

Geotechnical particle image velocimetry (GeoPIV), as a type of digital image correlation (DIC), represents the state-of-the-art methodology for non-contact full-field deformation measurement in geotechnical engineering. Yet, when applying GeoPIV on sand specimens with interests in grain level, the discontinuities detection at grain boundaries remains as a challenge for 2D GeoPIV applications. In order to facilitate the full-field measurement for microscopic study, a method is proposed in this study to realize 2D pixel-level motion calculation using supervised optical flow algorithm with deep networks. Using digital images acquired from direct shear testing, the performance of this approach is demonstrated and compared with the prevailing GeoPIV method. Two series of experiments using small and large displacement modes were conducted, respectively, to demonstrate the method’s ability of revealing greater insights on soil behavior at grain level. To verify its accuracy, performance benchmarking of the approach was also conducted. Besides, a method was proposed to evaluate the errors in experimental images to ensure the accuracy and precision. It was demonstrated that the proposed method can achieve accurate pixel-level motion field calculation using images of common size and that the deformation discontinuities among particles can be clearly presented.