Innovative Design Algorithm of Huizhou Bamboo Weaving Patterns Based on Deep Learning

In the field of innovative design of Huizhou bamboo weaving patterns, traditional deep learning algorithms cannot fully capture the fine structure and subtle changes of patterns, resulting in distorted or blurred results, and require a lot of computing resources and time during the training process. This paper constructs an improved ViT (Vision Transformer) model to collect diverse Huizhou bamboo weaving pattern data covering different styles and forms. In the data enhancement stage, common enhancement techniques such as rotation, scaling, flipping, and color perturbation are used to increase the diversity of training data. Based on the traditional ViT model, a local self-attention mechanism is applied to replace the traditional global self-attention mechanism. Mixed precision training and distributed training strategies are used to effectively accelerate the training process while maintaining high accuracy. The model automatically generates innovative designs by learning the style and structural characteristics of Huizhou bamboo weaving patterns, and adds a detail repair module in the generation process to enhance the detail expression of the pattern. The experimental results show that the improved ViT model tends to 0.95 after 50 training rounds, indicating that it performs well in detail preservation and structural similarity; with a sample data volume of 5000, the training time of the improved ViT model is 47.4 seconds, and the GPU memory usage is 37.1GB, providing higher computing efficiency. The experimental results prove the effectiveness of this paper’s research on the innovative design algorithm of Huizhou bamboo weaving patterns.

@article{Rong2025,
  title     = {Innovative Design Algorithm of Huizhou Bamboo Weaving Patterns Based on Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jinjin Rong and Xin Fang},
  doi       = {10.14569/IJACSA.2025.0160595},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160595}
}