Classification and recognition of the Nantong blue calico pattern based on deep learning
| Autor: | Ke-Ke Sun, Jing-Wan Huang, Yu-Yang Yuan, Ming-Yue Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Engineered Fibers and Fabrics, Vol 19 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1558-9250 15589250 |
| DOI: | 10.1177/15589250241270618 |
| Popis: | The Nantong blue calico pattern is a significant and indispensable part of China’s intangible cultural heritage, representing an artistic form of weaving and dyeing. However, existing research on blue calico patterns is not extensive, and few studies have focused on the construction of a database categorizing them or on recognizing the Nantong blue calico pattern. Obtaining good efficiency and accuracy through manual recognition has been the primary challenge in recognizing the Nantong blue calico pattern. In light of these challenges, this study proposes the use of deep learning network model to intelligently classify and recognize blue calico patterns.First, the patterns are classified to establish a Nantong blue calico pattern database, and the corresponding category labels are then manually assigned to each image. Second, based on the database and a backbone feature extraction network, the abilities of SSD (Single Shot Multibox Detector), Faster RCNN (Region-CNN), and You Only Look Once (to recognize the Nantong blue calico pattern were compared. The results show that the SSD model based on a VGG (Visual Geometry Group) backbone network has the best recognition accuracy of these three algorithms, with an average accuracy of 79.42%. On this basis, we selected the SSD model for parameter optimization and adjustment, and we replaced the backbone with mobilenetv2, a lighter backbone extraction network, to recognize the Nantong blue calico pattern. The results show that compared with the original SSD model, the optimized SSD model can improve the pattern recognition rate of Nantong blue calico pattern. Furthermore, this paper makes use of the characteristics of the VGG deep network, the backbone network of the SSD model, to efficiently extract the features of blue calico patterns, which provides a basis for designers to design innovative blue calico patterns. |
| Databáze: | Directory of Open Access Journals |
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