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<p>Stitches are the defining feature of Cantonese Embroidery, an intangible cultural heritage of China. Their diversity and high flexibility pose significant challenges for automatic recognition. A new framework for the recognition of Cantonese Embroidery stitches in real-world embroidery scenarios is proposed. First, the stitch regions are segmented and extracted from embroidery images using SAM2, and a dataset is then established. Afterward, the Squeeze-and-Excitation module is refined and incorporated into a CNN structure, leading to an enhanced Squeeze-and-Excitation Residual Neural Network for Cantonese Embroidery (SE-ResNet-CE) stitch recognition model. Through this design, joint attention to global and local stitch features is achieved. The experimental results reveal that the proposed model achieves a classification accuracy of 98.81% and a Macro-F1 score of 98.3% on the dataset. This method effectively extracts fine-grained features of stitches, providing technical support for the digitization of embroidery.</p>

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A stitch recognition based on ResNet and an attention mechanism for Cantonese Embroidery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Yongsheng Rao,
  • Shanmei Yang,
  • Qixin Zhou,
  • Ying Wang,
  • Bing Hu,
  • Ranran Wang,
  • Maoning Li

摘要

Stitches are the defining feature of Cantonese Embroidery, an intangible cultural heritage of China. Their diversity and high flexibility pose significant challenges for automatic recognition. A new framework for the recognition of Cantonese Embroidery stitches in real-world embroidery scenarios is proposed. First, the stitch regions are segmented and extracted from embroidery images using SAM2, and a dataset is then established. Afterward, the Squeeze-and-Excitation module is refined and incorporated into a CNN structure, leading to an enhanced Squeeze-and-Excitation Residual Neural Network for Cantonese Embroidery (SE-ResNet-CE) stitch recognition model. Through this design, joint attention to global and local stitch features is achieved. The experimental results reveal that the proposed model achieves a classification accuracy of 98.81% and a Macro-F1 score of 98.3% on the dataset. This method effectively extracts fine-grained features of stitches, providing technical support for the digitization of embroidery.