Image Classification and Restoration of Ancient Textiles Based on Convolutional Neural Network

Abstract

Abstract Ancient textile images have a variety of styles and themes, and the classification of different types of textiles provides a reliable reference for the protection and restoration of cultural relics. Due to the low efficiency of traditional classification methods and the low accuracy of classification, the image restoration of textiles takes longer and the repair effect is poor. Therefore, this paper takes ancient textile images as the research object and selects YOLOv4–ViT collaborative identification network (YOLOv4–ViT network) and generative adversarial networks (GAN) restoration model from a variety of network models to classify and restore ancient textile images. In this work, YOLOv4–ViT network is used to recognize and classify pattern elements in ancient textile images. Then, according to the classification results, restoration training of ancient textiles was carried out using an improved GAN restoration model, for which the final classification accuracy reached 92.78% and the repair result even took only 1.5 s. On this basis, a reliable retrieval and restoration system is designed to realize the repair of damaged textile images, reduce the difficulty of repair, and help users retrieve and browse different categories of ancient textile images, thus solve the problems of slow retrieval speed in traditional retrieval methods and poor restoration effect of ancient textile images.

Keywords

• Convolutional neural network
• Deep learning
• Ancient textile image classification
• YOLOv4-ViT network
• GAN