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<p>Dunhuang murals, as invaluable historical and cultural heritage, pose significant challenges in automatic classification due to their large volume, visual similarity, and deterioration over time. This study introduces SER-Net, a lightweight and efficient classification network optimized for real-time mural recognition on mobile devices. A specialized dataset covering nine dynasties—Early Tang, Northern Wei, Northern Zhou, Peak Tang, Sui, Late Tang, Middle Tang, Five Dynasties, and Western Wei—was manually constructed and augmented to address class imbalance. SER-Net is designed based on RepVGG and ResNet18, and incorporates the SED-Block module, which integrates squeeze-and-excitation (SE) attention and Channel-Shuffle mechanisms to improve feature representation. Moreover, the use of depthwise separable convolution significantly reduces the model parameters while maintaining accuracy. Experimental results demonstrate that SER-Net effectively balances model size, accuracy, and computational efficiency, making it suitable for deployment in resource-constrained environments.</p>

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Research and implementation of mural classification based on lightweight network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月3日

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  • Jin Zheng,
  • Manjun Zhang,
  • Yinghui Zhang,
  • Wuxin Yuan,
  • Zhao Xiaobing

摘要

Dunhuang murals, as invaluable historical and cultural heritage, pose significant challenges in automatic classification due to their large volume, visual similarity, and deterioration over time. This study introduces SER-Net, a lightweight and efficient classification network optimized for real-time mural recognition on mobile devices. A specialized dataset covering nine dynasties—Early Tang, Northern Wei, Northern Zhou, Peak Tang, Sui, Late Tang, Middle Tang, Five Dynasties, and Western Wei—was manually constructed and augmented to address class imbalance. SER-Net is designed based on RepVGG and ResNet18, and incorporates the SED-Block module, which integrates squeeze-and-excitation (SE) attention and Channel-Shuffle mechanisms to improve feature representation. Moreover, the use of depthwise separable convolution significantly reduces the model parameters while maintaining accuracy. Experimental results demonstrate that SER-Net effectively balances model size, accuracy, and computational efficiency, making it suitable for deployment in resource-constrained environments.