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基于残差注意力机制的泥石流沟谷识别

刘坤香, 王保云, 徐繁树, 韩俊. 基于残差注意力机制的泥石流沟谷识别[J]. 中国地质灾害与防治学报, 2022, 33(6): 134-141. doi: 10.16031/j.cnki.issn.1003-8035.202111010
引用本文: 刘坤香, 王保云, 徐繁树, 韩俊. 基于残差注意力机制的泥石流沟谷识别[J]. 中国地质灾害与防治学报, 2022, 33(6): 134-141. doi: 10.16031/j.cnki.issn.1003-8035.202111010
LIU Kunxiang, WANG Baoyun, XU Fanshu, HAN Jun. Debris flow gully recognition based on residual attention mechanism[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(6): 134-141. doi: 10.16031/j.cnki.issn.1003-8035.202111010
Citation: LIU Kunxiang, WANG Baoyun, XU Fanshu, HAN Jun. Debris flow gully recognition based on residual attention mechanism[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(6): 134-141. doi: 10.16031/j.cnki.issn.1003-8035.202111010

基于残差注意力机制的泥石流沟谷识别

  • 基金项目: 国家自然科学基金项目(61966040)
详细信息
    作者简介: 刘坤香(1996-),女,重庆万州人,硕士研究生,主要从事泥石流孕灾机制及机器学习研究。E-mail:960801608@qq.com
    通讯作者: 王保云(1977-),男,云南玉溪人,博士,副教授,主要从事机器学习及图像处理研究。E-mail:wspbmly@163.com
  • 中图分类号: P642.23

Debris flow gully recognition based on residual attention mechanism

More Information
  • 针对泥石流灾害沟谷图像分类问题,文章对Resnet18网络进行改进,提出了一种改进的卷积神经网络模型。通过在网络结构中加入残差注意力模块,解决了原模型提取图像特征较差、边缘模糊的问题,改进后的网络能精确捕捉到泥石流灾害沟谷图像中的轮廓和内部山脊信息。此外,文章还对多种注意力机制结构进行了实验对比,分析其差异性,得出最适合泥石流灾害沟谷数据分类的注意力机制网络。实验表明改进后的网络模型在泥石流灾害沟谷图像的分类准确率达到75.42%,其分类性能在Resnet18网络模型的基础上提升了5.1%。

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  • 图 1  基于残差注意力机制的泥石流灾害沟谷图像分类模型Resnet18_SC

    Figure 1. 

    图 2  空间注意力机制模块

    Figure 2. 

    图 3  通道注意力机制模块

    Figure 3. 

    图 4  通道和空间注意力机制结合的CBAM模块

    Figure 4. 

    图 5  泥石流数据集展示

    Figure 5. 

    图 6  Resnet18_CS和Resnet18_SC 准确率和损失对比曲线

    Figure 6. 

    图 7  SC注意力机制结果可视化

    Figure 7. 

    表 1  Alexnet与Alexnet_CBAM结果对比

    Table 1.  Comparison of Alexnet and Alexnet CBAM results

    模型特异性/%灵敏度/%损失准确率/%
    Alexnet60.3760.080.039461.29
    Alexnet_CBAM61.8162.150.037363.44
    下载: 导出CSV

    表 2  VGG16与VGG16_CBAM结果对比

    Table 2.  Comparison of VGG16 and VGG16_CBAM results

    模型特异性/%灵敏度/%损失准确率/%
    VGG1661.4161.520.037162.72
    VGG16_CBAM60.7662.860.035665.23
    下载: 导出CSV

    表 3  Resnet18与Resnet18_CBAM结果对比

    Table 3.  Comparison of Resnet18 and Resnet18_CBAM results

    模型特异性/%灵敏度/%损失准确率/%
    Resnet1887.2669.880.028970.32
    Resnet18_CBAM87.9971.350.026173.12
    下载: 导出CSV

    表 4  不同注意力机制模块结果对比

    Table 4.  Comparison of results of different attentional mechanism modules

    模型参数量/106时间准确度/%损失
    Resnet18_C11.2724 m 17 s70.560.0269
    Resnet18_S11.2724 m 11 s71.170.0280
    Resnet18_CS11.2722 m 42 s73.120.0266
    Resnet18_SC11.2721 m 31 s75.420.0248
    下载: 导出CSV
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出版历程
收稿日期:  2021-11-05
修回日期:  2022-04-11
刊出日期:  2022-12-25

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