Traditional craftsmanship and culture are facing a transformation in modern science and technology development, and the cultural industry is gradually stepping into the digital era, which can realize the sustainable development of intangible cultural heritage with the help of digital technology. To innovatively generate wickerwork pattern design schemes that meets the user’s preferences, this study proposes a design method of wickerwork patterns based on a style migration algorithm. First, an image recognition experiment using residual network (ResNet) based on the convolutional neural network is applied to the Funan wickerwork patterns to establish an image recognition model. The experimental results illustrate that the optimal recognition rate is 93.37% for the entire dataset of ResNet50 of the pattern design images, where the recognition rate of modern patterns is 89.47%, while the recognition rate of traditional patterns is 97.14%, the recognition rate of wickerwork patterns is 95.95%, and the recognition rate of personality is 90.91%. Second, based on Cycle-Consistent Adversarial Networks (CycleGAN) to build design scheme generation models of the Funan wickerwork patterns, CycleGAN can automatically and innovatively generate the pattern design scheme that meets certain style characteristics. Finally, the designer uses the creative images as the inspiration source and participates in the detailed adjustment of the generated images to design the wickerwork patterns with various stylistic features. This proposed method could explore the application of AI technology in wickerwork pattern development, and providing more comprehensive and rich new material for the creation of wickerwork patterns, thus contributing to the sustainable development and innovation of traditional Funan wickerwork culture. In fact, this digital technology can empower the inheritance and development of more intangible cultural heritages.
Protecting and inheriting local traditional handicrafts and developing them into characteristic handicraft industries plays a certain role in maintaining social harmony and stability. This study proposes an innovative design method for wickerwork patterns to achieve the sustainable development of wickerwork handicraft culture. In order to accurately grasp the emotional perception law of wickerwork handicraft patterns and creatively generate wickerwork pattern design schemes in accordance with the user’s emotional preference, a wickerwork pattern design method based on deep learning is proposed. Firstly, the image recognition model of the Funan wickerwork patterns is established by using the ResNet. The experimental results show that the best recognition rate of ResNet34 for the whole pattern design image dataset is 94.36%, the recognition rate of modern patterns is 95.92%, and the recognition rate of traditional wickerwork patterns is 93.45%. Secondly, based on deep convolution generative adversarial network (DCGAN), a design scheme generation model of Funan wickerwork patterns is built. DCGAN can automatically and creatively generate pattern design schemes that can effectively stimulate consumers’ emotional feelings. Finally, the designer uses creative pictures as a source of inspiration, innovates the design of the generated images, and designs wickerwork patterns with exquisite personality. This proposed method will increase the diversity of patterns and promote the sustainable development of traditional wickerwork techniques. Moreover, this proposed method can help design companies identify customers’ psychological needs and support designers in innovatively and efficiently creating new cultural innovation design solutions.
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