Scholarlink

<p>To improve sketch recognition accuracy, this study proposes an enhanced sketch recognition model based on an improved CycleGAN network and a dual attention mechanism. The proposed model first incorporates multi-directional convolution and brightness equalization modules into the CycleGAN network to extract edge and contour features. A dual attention mechanism is then implemented using channel attention and spatial attention modules, effectively addressing issues of sparse strokes and uneven spatial distribution in sketches while enhancing the representation of critical features. Finally, a hybrid architecture combining global average pooling and convolution layers serves as the classifier to produce sketch recognition results. Simulation results demonstrate that this model achieves 97.08% accuracy, 98.12% precision, 98.23% recall, and 97.45% F1 score for sketch recognition on the TU-Berlin dataset, and 98.65% accuracy, 98.12% precision, 98.76% recall, and 97.95% F1 score on the QuickDraw dataset. Compared with state-of-the-art sketch recognition models, this model exhibits superior performance in accuracy. These results indicate that the model can enhance sketch recognition precision and provide technical support for converting sketches into high-quality animated images.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Sketch recognition model based on improved CycleGAN network and dual attention mechanism

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

查看全文
  • Yun Wang,
  • Li Xie,
  • Mengyi Huang

摘要

To improve sketch recognition accuracy, this study proposes an enhanced sketch recognition model based on an improved CycleGAN network and a dual attention mechanism. The proposed model first incorporates multi-directional convolution and brightness equalization modules into the CycleGAN network to extract edge and contour features. A dual attention mechanism is then implemented using channel attention and spatial attention modules, effectively addressing issues of sparse strokes and uneven spatial distribution in sketches while enhancing the representation of critical features. Finally, a hybrid architecture combining global average pooling and convolution layers serves as the classifier to produce sketch recognition results. Simulation results demonstrate that this model achieves 97.08% accuracy, 98.12% precision, 98.23% recall, and 97.45% F1 score for sketch recognition on the TU-Berlin dataset, and 98.65% accuracy, 98.12% precision, 98.76% recall, and 97.95% F1 score on the QuickDraw dataset. Compared with state-of-the-art sketch recognition models, this model exhibits superior performance in accuracy. These results indicate that the model can enhance sketch recognition precision and provide technical support for converting sketches into high-quality animated images.