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<p>Cantonese embroidery images are difficult to recognize accurately because of significant differences in their morphologies and complex backgrounds. In this paper, a dedicated Cantonese embroidery image dataset is constructed, and an improved YOLOv8 detection method is proposed for the accurate detection of objects in Cantonese embroidery images. By integrating a feature enhancement module and the large separable kernel attention (LSKA) mechanism, the method strengthens its capacity for identifying key texture regions, enhances feature extraction in complex backgrounds, and optimizes performance through lightweight design and the WIoU loss function. The experimental results show that the method achieves a detection accuracy of 98.5%, with mAP and F1 scores improved by 0.7% and 1.37%, respectively, compared with those of the original model, and the model size is reduced by 5.12%. This provides an effective solution for the automated detection of intangible cultural heritage embroidery and the recognition of other heritage artifacts.</p>

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Feature enhancement and attention mechanism fusion method for Cantonese embroidery image detection based on YOLOv8

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Yongsheng Rao,
  • Ting An,
  • Yingshuang Xuan,
  • Ranran Wang,
  • Qixin Zhou,
  • Hao Guan,
  • Maoning Li

摘要

Cantonese embroidery images are difficult to recognize accurately because of significant differences in their morphologies and complex backgrounds. In this paper, a dedicated Cantonese embroidery image dataset is constructed, and an improved YOLOv8 detection method is proposed for the accurate detection of objects in Cantonese embroidery images. By integrating a feature enhancement module and the large separable kernel attention (LSKA) mechanism, the method strengthens its capacity for identifying key texture regions, enhances feature extraction in complex backgrounds, and optimizes performance through lightweight design and the WIoU loss function. The experimental results show that the method achieves a detection accuracy of 98.5%, with mAP and F1 scores improved by 0.7% and 1.37%, respectively, compared with those of the original model, and the model size is reduced by 5.12%. This provides an effective solution for the automated detection of intangible cultural heritage embroidery and the recognition of other heritage artifacts.