Wrinkle Defect

Wrinkle defects, encompassing both facial wrinkles and those in materials like composite laminates, are a focus of current research aiming to improve detection, measurement, and modeling. Studies utilize convolutional neural networks for automated wrinkle segmentation in images and employ techniques like homogenization and finite element analysis to predict the mechanical behavior of wrinkled materials, with non-destructive testing methods like Shearography and fringe projection profilometry used for measurement. This research is significant for applications ranging from automated skin analysis and diagnostics to improving the structural integrity assessment of industrial products and enhancing the realism of virtual try-on systems and 3D facial models.