Video extensometry measurement method for materials with large deformations

This paper addresses the issue of segmentation errors in video extensometer caused by deformation, dispersion, and cracks in markers during the stretching process. A method combining frame -to-frame matching with deep learning is proposed to address the high robustness segmentation problem of black and white markers on materials undergoing large strains. The selection of template position during image matching and the update of the template throughout the stretching process are discussed. Experiments and analysis were conducted on various types of rubber and plastic specimens that exhibit significant strain and irregular deformation. The results demonstrate that this method can be applied to line marker-type video extensometers, enhancing the overall robustness of the measurement algorithm.