Repetitive Patterns Recognition in Textures of Ancient Peruvian Pottery

We present a study and comparison of computer vision methods for the task of finding repetitive motifs in ancient Peruvian pottery. Under this context, the main difficulties for solving the task are that the motifs are in most cases highly repetitive, that the motifs corresponding to the same pattern are slightly different due to be hand-drawn, and that the amount of data available for training and testing purposes is scarce. We evaluate and compare several techniques: Template Matching, Segment Anything Model, Mask R-CNN, Faster R-CNN, RetinaNet, and YoloV8-s. We conclude that YoloV8-s and RetinaNet are the most effective techniques for the task, but the effectiveness for zero-shot detection is low for all evaluated techniques.