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基于CNN的多尺寸航拍图像定位方法的研究与实现

潘海侠 徐嘉璐 李锦涛 王赟豪 王华锋

潘海侠, 徐嘉璐, 李锦涛, 等 . 基于CNN的多尺寸航拍图像定位方法的研究与实现[J]. 北京航空航天大学学报, 2019, 45(11): 2170-2176. doi: 10.13700/j.bh.1001-5965.2019.0045
引用本文: 潘海侠, 徐嘉璐, 李锦涛, 等 . 基于CNN的多尺寸航拍图像定位方法的研究与实现[J]. 北京航空航天大学学报, 2019, 45(11): 2170-2176. doi: 10.13700/j.bh.1001-5965.2019.0045
PAN Haixia, XU Jialu, LI Jintao, et al. Research and implementation of multi-size aerial image positioning method based on CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2170-2176. doi: 10.13700/j.bh.1001-5965.2019.0045(in Chinese)
Citation: PAN Haixia, XU Jialu, LI Jintao, et al. Research and implementation of multi-size aerial image positioning method based on CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2170-2176. doi: 10.13700/j.bh.1001-5965.2019.0045(in Chinese)

基于CNN的多尺寸航拍图像定位方法的研究与实现

doi: 10.13700/j.bh.1001-5965.2019.0045
详细信息
    作者简介:

    潘海侠  女, 博士, 副教授。主要研究方向:人工智能、模式识别、计算机视觉与图像处理

    王华锋  男, 博士, 副教授。主要研究方向:图像识别、基于图像的测量

    通讯作者:

    王华锋.E-mail:wanghuafeng@buaa.edu.cn

  • 中图分类号: V221+.3; TB553

Research and implementation of multi-size aerial image positioning method based on CNN

More Information
  • 摘要:

    图像定位常用于无人机视觉导航,传统的无人机视觉导航广泛采用景象匹配导航方式,随着计算机技术的不断发展,深度学习技术为视觉导航的实现提供了新途径。以无人机的垂直侦查为背景,将飞行区域的航拍图像划分成大小相同的若干网格,每个网格代表一类区域,用网格图像制作数据集训练卷积神经网络(CNN)。基于AlexNet设计了一种融合显著性特征的全卷积网络模型,有效实现了一个基于CNN的多尺寸输入的滑动窗口分类器,并提出了一种邻域显著性参照定位策略来筛选分类结果,从而实现多尺寸航拍图像的定位。

     

  • 图 1  FCN滑动窗口形式

    Figure 1.  FCN sliding window

    图 2  航拍图像的显著性特征图

    Figure 2.  Saliency feature map of aerial image

    图 3  Multi-channel AlexNet-FCN示意图

    Figure 3.  Schematic diagram of multi-channel AlexNet-FCN

    图 4  三维曲面样例

    Figure 4.  3D surface sample diagram

    图 5  显著性权重示意图

    Figure 5.  Schematic diagram of saliency weight

    图 6  概率矩阵热力图

    Figure 6.  Heat map of probability matrix

    图 7  哈尔滨市的航拍图像

    Figure 7.  Aerial image of Harbin

    图 8  ROC曲线

    Figure 8.  ROC curves

    表  1  查询图像集准确率

    Table  1.   Query image set accuracy

    方法 输入 准确率/% 所用环境 平均运行时间/s
    AlexNet-FCN RGB 94.1 0.017
    multi-channel
    AlexNet-FCN
    RGB+显著特征
    RGB+HOG
    RGB+LBP
    95.4
    94.6
    94.2
    GPU: Titan X
    Pytorch
    0.022
    0.191
    0.034
    SIFT
    SURF
    ORB
    RGB 63.3
    73.6
    61.9
    CPU: E5-2670 37.865
    31.25
    44.586
    下载: 导出CSV

    表  2  航拍图像为512×512大小时top-k下的准确率

    Table  2.   Accuracy at top-k when aerial image size is 512×512

    是否使用邻域显著性参照定位 区域所占完整网格比例/% 准确率/%
    k=1 k=2 k=3 k=4
    100 67.3 39.5 26.6 19.9
    ≥80 81.2 68.1 49.3 38.0
    ≥60 83.9 74.1 56.0 43.6
    ≥40 85.1 76.0 58.3 45.5
    ≥20 85.6 77.1 59.8 46.7
    ≥0 86.1 77.9 61.1 47.9
    100 69.4 40.1 27.0 20.3
    ≥80 83.1 69.0 49.9 38.2
    ≥60 85.3 75.0 56.7 44.0
    ≥40 86.3 77.0 59.0 45.8
    ≥20 86.7 77.8 60.4 47.1
    ≥0 87.1 78.5 61.6 48.3
    下载: 导出CSV

    表  3  航拍图像为768×768大小时top-k下的准确率

    Table  3.   Accuracy at top-k when aerial image size is 768×768

    是否使用邻域显著性参照定位 区域所占完整网格比例/% 准确率/%
    k=1 k=2 k=3 k=4
    100 65.7 62.1 59.6 55.4
    ≥80 94.6 93.3 91.3 88.2
    ≥60 95.7 94.9 93.3 90.5
    ≥40 96.8 95.8 94.3 91.7
    ≥20 97.2 96.2 94.8 92.3
    ≥0 97.6 96.6 95.3 93.2
    100 68.8 65.8 57.5 51.0
    ≥80 95.7 94.2 89.2 84.3
    ≥60 96.6 95.7 91.3 87.4
    ≥40 97.6 96.5 92.4 88.8
    ≥20 97.8 96.7 92.8 89.6
    ≥0 98.1 97.1 93.5 90.0
    下载: 导出CSV

    表  4  航拍图像为512×512大小时top-k下的召回率

    Table  4.   Recall rate at top-k when aerial image size is 512×512

    是否使用邻域显著性参照定位 区域所占完整网格比例/% 召回率/%
    k=1 k=2 k=3 k=4
    100 67.3 79.1 79.8 79.8
    ≥80 46.1 72.3 76.9 78.2
    ≥60 29.7 51.3 57.1 58.9
    ≥40 20.0 35.8 41.3 43.0
    ≥20 14.8 26.8 31.6 33.1
    ≥0 8.6 15.5 18.2 19.0
    100 69.4 80.3 81.1 81.1
    ≥80 47.2 73.4 77.9 78.9
    ≥60 30.1 51.8 57.8 59.3
    ≥40 20.3 36.2 41.8 43.3
    ≥20 15.0 27.1 31.9 33.3
    ≥0 8.7 15.6 18.3 19.1
    下载: 导出CSV

    表  5  航拍图像为768×768大小时top-k下的召回率

    Table  5.   Recall rate at top-k when aerial image size is 768×768

    是否使用邻域显著性参照定位 区域所占完整网格比例/% 召回率/%
    k=1 k=2 k=3 k=4
    100 21.5 40.5 58.1 72.3
    ≥80 16.6 32.6 47.9 61.5
    ≥60 12.6 25.0 36.9 47.7
    ≥40 10.1 20.0 29.5 38.2
    ≥20 8.1 16.0 23.7 30.8
    ≥0 5.6 11.0 16.3 21.0
    100 22.6 43.1 61.5 75.1
    ≥80 16.8 32.9 48.5 62.3
    ≥60 12.8 25.3 37.3 48.1
    ≥40 10.2 20.1 29.7 38.5
    ≥20 8.2 16.1 23.8 31.0
    ≥0 5.6 11.1 16.4 21.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-13
  • 录用日期:  2019-06-21
  • 刊出日期:  2019-11-20

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