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面向光学遥感目标的全局上下文检测模型设计

张瑞琰 姜秀杰 安军社 崔天舒

张瑞琰, 姜秀杰, 安军社, 崔天舒. 面向光学遥感目标的全局上下文检测模型设计[J]. 中国光学, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057
引用本文: 张瑞琰, 姜秀杰, 安军社, 崔天舒. 面向光学遥感目标的全局上下文检测模型设计[J]. 中国光学, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057
ZHANG Rui-yan, JIANG Xiu-jie, AN Jun-she, CUI Tian-shu. Design of global-contextual detection model for optical remote sensing targets[J]. Chinese Optics, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057
Citation: ZHANG Rui-yan, JIANG Xiu-jie, AN Jun-she, CUI Tian-shu. Design of global-contextual detection model for optical remote sensing targets[J]. Chinese Optics, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057

面向光学遥感目标的全局上下文检测模型设计

doi: 10.37188/CO.2020-0057
基金项目: 中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(No. Y42613A32S)
详细信息
    作者简介:

    张瑞琰(1995—),女,河南商丘人,博士研究生,2016年于南开大学获得理学学士学位,2018年于中国科学院大学国家空间科学中心转为硕博连读,主要从事空间数据处理、遥感目标检测网络优化及压缩的相关研究。E-mail:zhangruiyan16@mails.ucas.ac.cn

    姜秀杰(1965—),女,黑龙江鹤岗人,博士,研究员,博士生导师,1988年于北京航空航天大学获得工学学士学位,1991年于中国科学院大学获得工学硕士学位,2007年于清华大学获得工学博士学位,主要从事火箭探空技术研究、电场探测技术研究和空间综合电子技术研究。E-mail:jiangxj@nssc.ac.cn

  • 中图分类号: TP391.4

Design of global-contextual detection model for optical remote sensing targets

Funds: Supported by Laboratory Fund of Key Laboratory of Electronics and Information Technology for Space Systems, CAS (No. Y42613A32S)
More Information
  • 摘要: 在复杂背景下的光学遥感图像目标检测中,为了提高检测精度,同时降低检测网络复杂度,提出了面向光学遥感目标的全局上下文检测模型。首先,采用结构简单的特征编码-特征解码网络进行特征提取。其次,为提高对多尺度目标的定位能力,采取全局上下文特征与目标中心点局部特征相结合的方式生成高分辨率热点图,并利用全局特征实现目标的预分类。最后,提出不同尺度的定位损失函数,用于增强模型的回归能力。实验结果表明: 当使用主干网络Root-ResNet18时,本文模型在公开遥感数据集NWPU VHR-10上的检测精度可达97.6%AP50和83.4%AP75,检测速度达16 PFS,基本满足设计需求,实现了网络速度和精度的有效平衡,便于后续算法在移动设备端的移植和应用。
  • 图  1  特征编码-特征解码网络架构

    Figure  1.  Framework of the feature encoder-feature decoder network

    图  2  全局上下文检测模型总体架构

    Figure  2.  Overall framework of the global-contextual detection model

    图  3  普通卷积采样和变形卷积采样示意图

    Figure  3.  Sampling diagrams in standard convolution and deformation convolution

    图  4  全局上下文特征提取流程

    Figure  4.  Flow chart of global-contextual feature extraction

    图  5  (a)添加目标框的原图及(b)高斯椭圆掩模示意图

    Figure  5.  (a) Original image with a target box and (b) schematic diagram of gaussian elliptical mask

    图  6  不同γ值对结果的影响

    Figure  6.  Effects of different γ values on results

    图  7  不同ν值对结果的影响

    Figure  7.  Effects of different ν values on results

    图  8  GCDN的可视化检测效果图

    Figure  8.  Visual detection results of the GCDN

    表  1  ResNet18与Root-ResNet18结构

    Table  1.   Structures of ResNet18 and Root-ResNet18

    阶段输出尺寸ResNet18Root-ResNet18
    C1128×1287×7, 643×3, 64
    3×3, 64
    3×3, 64
    3×3, MaxPool
    $\left[ {\begin{array}{*{20}{c}} {3 \times 3,64} \\ {3 \times 3,64} \end{array}} \right]\times 2$
    C264×643×3, MaxPool$\left[ {\begin{array}{*{20}{c}} {3 \times 3,128} \\ {3 \times 3,128} \end{array}} \right]\times 2$
    $\left[ {\begin{array}{*{20}{c}} {3 \times 3,64} \\ {3 \times 3,64} \end{array}} \right]\times 2$
    C332×32$\left[ {\begin{array}{*{20}{c}} {3 \times 3,128} \\ {3 \times 3,128} \end{array}} \right]\times 2$$\left[ {\begin{array}{*{20}{c}} {3 \times 3,256} \\ {3 \times 3,256} \end{array}} \right]\times 2$
    C416×16$\left[ {\begin{array}{*{20}{c}} {3 \times 3,256} \\ {3 \times 3,256} \end{array}} \right]\times 2$$\left[ {\begin{array}{*{20}{c}} {3 \times 3,512} \\ {3 \times 3,512} \end{array}} \right]\times 2$
    C58×8$\left[ {\begin{array}{*{20}{c}} {3 \times 3,512} \\ {3 \times 3,512} \end{array}} \right]\times 2$
    下载: 导出CSV

    表  2  数据集NWPU VHR-10目标尺寸统计表

    Table  2.   Statistics of target sizes in the NWPU VHR-10 dataset

    尺度(pixel)0−1010−4040−100100−300300−500500以上
    00.13270.69480.15990.01260
    00.14480.72050.12420.01050
    下载: 导出CSV

    表  3  不同模型在数据集NWPU VHR-10上的平均精确度对比

    Table  3.   Comparison of mean average precisions of different models in the NWPU VHR-10 dataset

    模型主干网络飞机船舰油罐棒球场网球场篮球场田径场港口桥梁车辆平均精确度(mAP)
    RICAODAlexNet0.99700.90800.90610.92910.90290.80310.90810.80290.68530.87140.8712
    SSDVGG160.98390.89930.89180.98510.87910.84810.99490.77300.78280.87390.8912
    YOLOv3Darknet530.90910.90910.90810.99130.90860.90910.99470.90050.90910.90350.9243
    MMDFNVGG160.99340.92270.99180.96680.96320.97561.00000.97400.80270.91360.9504
    MSDNResNet500.99760.97210.83830.99090.97340.99910.98680.97190.92670.90100.9558
    MSCNNResNet500.99400.95300.91800.96300.95400.96700.99300.95500.97200.93300.9600
    GCDNRoot-ResNet180.99910.99830.96770.99160.99910.97590.99880.94120.92240.96360.9757
    下载: 导出CSV

    表  4  检测阈值0.75下的不同模型平均精确度对比

    Table  4.   Comparison of mean-average precision of different models under the 0.75 detection threshold

    模型平均准确率 (AP)平均准确率(AP)($I_{ {\rm{{ou} } } }$=0.50:0.95)平均召回率(AR)($I_{ {\rm{{ou} } } }$=0.50:0.95)
    $I_{ {\rm{{ou} } } }$=0.50:0.95$I_{ {\rm{{ou} } } }$=0.50$I_{ {\rm{{ou} } } }$=0.75小目标中目标大目标小目标中目标大目标
    CenterNet0.6630.9680.7680.5760.6690.7040.6300.7250.741
    MSCNN0.7060.9600.8240.5470.5780.7010.6000.6050.700
    GCDN-woGC0.6790.9730.7750.5720.6900.7500.6390.7440.786
    GCDN0.7050.9760.8340.6120.7270.7060.6830.7730.740
    下载: 导出CSV

    表  5  不同模型的平均检测时间对比

    Table  5.   Comparison of the average detection times with different models

    模型输入尺寸时间/s
    RICAOD400×4002.89
    MMDFN400×4000.75
    YOLOv3640×6400.13
    MSDN600×8000.21
    GCDN640×6400.06
    下载: 导出CSV

    表  6  不同模型在数据集DOTA上的平均精确度对比

    Table  6.   Comparison of the mean-average precisions with different models in the DOTA dataset

    模型主干网络平均检测精度(mAP)
    YOLOv2GoogleNet39.20
    R-FCNResNet10152.58
    Faster-RCNNResNet10160.46
    SCFPN-scfResNet10175.22
    GCDNRoot-ResNet1875.95
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-07
  • 修回日期:  2020-05-11
  • 网络出版日期:  2020-10-22
  • 刊出日期:  2020-12-01

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