Scholarlink

<p>This paper proposes an enhanced DenseNet-based strategy to improve mural classification performance. Briefly, a new independent component layer structure separates the larger 7 × 7 convolution kernels in the input layer into the trunk of three series of 3 × 3 small convolution kernels. The original ReLU activation is replaced with Mish activation. An attention-based feature filtering aggregation bottleneck is designed and the maximum pooling layer is used instead of global average pooling. A fully connected layer is added for richer contour and shape feature extraction. The accuracy, precision, recall and F1 value of the model are 93.07%, 93.86%, 93.08% and 93.37%, respectively, whose accuracy increased by 7.26% compared with the original DenseNet-121 model. The proposed model outperforms mainstream classification algorithms to a certain extent in terms of classification accuracy, generalization ability and stability, which is highly important for the large-scale screening and protection of mural images and the construction of digital museums.</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

Mural classification network based on the fusion of shallow feature enhancement and dense attention aggregation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

查看全文
  • Jianfang Cao,
  • Cunhe Peng,
  • Zhuolin Yang,
  • Fang Wang

摘要

This paper proposes an enhanced DenseNet-based strategy to improve mural classification performance. Briefly, a new independent component layer structure separates the larger 7 × 7 convolution kernels in the input layer into the trunk of three series of 3 × 3 small convolution kernels. The original ReLU activation is replaced with Mish activation. An attention-based feature filtering aggregation bottleneck is designed and the maximum pooling layer is used instead of global average pooling. A fully connected layer is added for richer contour and shape feature extraction. The accuracy, precision, recall and F1 value of the model are 93.07%, 93.86%, 93.08% and 93.37%, respectively, whose accuracy increased by 7.26% compared with the original DenseNet-121 model. The proposed model outperforms mainstream classification algorithms to a certain extent in terms of classification accuracy, generalization ability and stability, which is highly important for the large-scale screening and protection of mural images and the construction of digital museums.