Periodic Pattern Detection of Printed Fabric Based on Deep Learning Algorithm

Abstract

Abstract Printed fabric patterns contain multiple repeat pattern primitives, which have a significant impact on fabric pattern design in the textile industry. The pattern primitive is often composed of multiple elements, such as color, form, and texture structure. Therefore, the more pattern elements it contains, the more complex the primitive is. In order to segment fabric primitives, this paper proposes a novel convolutional neural network (CNN) method with spatial pyramid pooling module as a feature extractor, which enables to learn the pattern feature information and determine whether the printed fabric has periodic pattern primitives. Furthermore, by choosing pair of activation peaks in a filter, a set of displacement vectors can be calculated. The activation peaks that are most accordant with the optimum displacement vector contribute to pick out the final size of primitives. The results show that the method with the powerful feature extraction capabilities of the CNN can segment the periodic pattern primitives of complex printed fabrics. Compared with the traditional algorithm, the proposed method has higher segmentation accuracy and adaptability.