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基于双重注意力机制的滑坡识别方法优化

吴琪 周创兵 黄发明 姚池

吴琪, 周创兵, 黄发明, 姚池. 基于双重注意力机制的滑坡识别方法优化[J]. 地质科技通报, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053
引用本文: 吴琪, 周创兵, 黄发明, 姚池. 基于双重注意力机制的滑坡识别方法优化[J]. 地质科技通报, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053
Wu Qi, Zhou Chuangbing, Huang Faming, Yao Chi. Optimization of the landslide identification method based on a dual attention mechanism[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053
Citation: Wu Qi, Zhou Chuangbing, Huang Faming, Yao Chi. Optimization of the landslide identification method based on a dual attention mechanism[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053

基于双重注意力机制的滑坡识别方法优化

doi: 10.19509/j.cnki.dzkq.2022.0053
基金项目: 

中国中铁股份公司重点课题 2019-重点-42-01

详细信息
    作者简介:

    吴琪(1995—), 男,现正攻读水利工程专业硕士学位,主要从事深度学习与岩土工程方面的研究工作。E-mail: qi.wu@email.ncu.edu.cn

    通讯作者:

    姚池(1986—),男,教授,博士生导师,主要从事裂隙岩体多物理场耦合研究工作。E-mail: chi.yao@ncu.edu.cn

  • 中图分类号: P642.22

Optimization of the landslide identification method based on a dual attention mechanism

  • 摘要: 随着计算机视觉技术的发展, 通过卫星图像深度学习进行滑坡识别的研究正在逐步展开。通过引入双重注意力机制, 提出了一种基于卷积神经网络的滑坡图像识别优化算法。基于统计的2 200张滑坡图像数据集, 探讨了10种网络结构及4种注意力机制对滑坡识别结果的影响, 并通过比例为4∶1的训练集和测试集进行滑坡识别, 验证了本文方法的有效性。结果表明: ResNet结构相较于其他网络结构表现更为优秀, 就该算例而言, ResNet-101结构具有最高的召回率、精确率和F1度量。融入了双重注意力机制的卷积神经网络相较于单个神经网络而言, 滑坡识别的精确率更大, 且滑坡边界分割结果更接近于真实的滑坡边界, 其中, ResNet-101+DAN模型为最优模型。相较之下, 单个神经网络无法克服图像噪声的影响, 图像分割结果不佳。

     

  • 图 1  滑坡识别全卷积网络结构

    Figure 1.  FCN architecture for image recognition of landslides

    图 2  双重注意力机制网络结构

    Figure 2.  Network structures of the dual attention module

    图 3  空间注意力模块和通道注意力模块结构

    Figure 3.  Structure of the space attention module and channel attention module

    图 4  滑坡数据集标注结果

    Figure 4.  Annotation results of the landslide dataset

    图 5  不同注意力机制的滑坡识别结果对比

    Figure 5.  Comparison of landslide identification results of different attention modules

    表  1  开源数据对比结果

    Table  1.   Comparison results of open data

    网络结构 均交并比(MIoU)
    FCN 61.23
    DeepLab-v2 70.32
    ResNet50 73.27
    PSPNet+Res101 77.48
    DANet+Res101 79.76
    下载: 导出CSV

    表  2  不同网络架构的预测结果

    Table  2.   Prediction results of different network architectures

    序号 网络结构 精确率
    (Precision)
    召回率
    (Recall)
    F1度量
    (F1-measure)
    均交并比
    (MIoU)
    1 VGG-13 0.921 0.913 0.917 0.663
    2 VGG-16 0.933 0.894 0.913 0.705
    3 VGG-19 0.918 0.872 0.894 0.652
    4 ResNet-18 0.946 0.896 0.920 0.716
    5 ResNet-50 0.937 0.902 0.919 0.711
    6 ResNet-101 0.949 0.915 0.932 0.731
    7 Inception-v3 0.941 0.912 0.926 0.737
    8 DenseNet-121 0.963 0.852 0.904 0.723
    9 DenseNet-169 0.932 0.873 0.902 0.697
    10 DenseNet-201 0.923 0.903 0.913 0.743
    下载: 导出CSV

    表  3  不同方法实验结果对比

    Table  3.   Comparison of experimental results of different methods

    网络结构 精确率(Precision) 召回率(Recall) F1度量(F1-measure) 均交并比(MIoU) 检测速度/(帧·s-1)
    ResNet-101 0.949 0.915 0.917 0.731 14.0
    ResNet-101+SE 0.952 0.917 0.934 0.763 11.3
    ResNet-101+BAM 0.954 0.920 0.937 0.796 12.8
    ResNet-101+CBAM 0.961 0.923 0.942 0.787 11.5
    ResNet-101+DAN 0.964 0.952 0.958 0.802 13.6
    下载: 导出CSV

    表  4  不同类型滑坡计算结果的对比

    Table  4.   Comparison of calculation results of different types of landslides

    滑坡类型 精确率
    (Precision)
    召回率
    (Recall)
    F1度量
    (F1-measure)
    均交并比
    (MIoU)
    有植被覆盖土质滑坡 0.951 0.963 0.957 0.830
    无植被覆盖土质滑坡 0.932 0.897 0.914 0.782
    有植被覆盖岩质滑坡 0.974 0.921 0.947 0.812
    无植被覆盖岩质滑坡 0.923 0.903 0.913 0.776
    下载: 导出CSV
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  • 收稿日期:  2021-11-11

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