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基于改进Faster R-CNN的SAR图像飞机检测算法

李广帅 苏娟 李义红

李广帅, 苏娟, 李义红等 . 基于改进Faster R-CNN的SAR图像飞机检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 159-168. doi: 10.13700/j.bh.1001-5965.2020.0004
引用本文: 李广帅, 苏娟, 李义红等 . 基于改进Faster R-CNN的SAR图像飞机检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 159-168. doi: 10.13700/j.bh.1001-5965.2020.0004
LI Guangshuai, SU Juan, LI Yihonget al. An aircraft detection algorithm in SAR image based on improved Faster R-CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 159-168. doi: 10.13700/j.bh.1001-5965.2020.0004(in Chinese)
Citation: LI Guangshuai, SU Juan, LI Yihonget al. An aircraft detection algorithm in SAR image based on improved Faster R-CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 159-168. doi: 10.13700/j.bh.1001-5965.2020.0004(in Chinese)

基于改进Faster R-CNN的SAR图像飞机检测算法

doi: 10.13700/j.bh.1001-5965.2020.0004
详细信息
    作者简介:

    李广帅  男, 硕士研究生。主要研究方向:基于深度学习的目标检测

    苏娟  女, 博士, 副教授, 硕士生导师。主要研究方向:遥感图像处理、模式识别等

    通讯作者:

    苏娟, E-mail: suj04@mails.tsinghua.edu.cn

  • 中图分类号: TP751

An aircraft detection algorithm in SAR image based on improved Faster R-CNN

  • 摘要:

    在合成孔径雷达(SAR)图像分析领域,飞机作为一种重要目标,对其的检测越来越受到重视。针对传统SAR图像飞机检测算法需要人工设计特征且鲁棒性较差的问题,提出了一种基于改进Faster R-CNN的SAR图像飞机检测算法。制作了一个SAR图像飞机数据集(SAD),以Faster R-CNN为检测框架,利用改进k-means算法设计更合理的先验锚点框,以适应飞机目标的形状特点;借鉴inception模块思想,设计多路不同尺寸卷积核以扩展网络宽度,增强对浅层特征的表达;分析残差网络Layer5层的特征输出具有更大的感受野,对其上采样后进行特征融合以利用更多的上下文信息;同时引入Mask R-CNN算法中提出的RoI Align单元,消除特征图与原始图像的映射偏差。实验结果表明:相比原始的Faster R-CNN算法,所提改进的Faster R-CNN算法在SAR图像飞机数据集上平均检测精度提高了7.4%,同时保持了较快的检测速度。

     

  • 图 1  Faster R-CNN检测流程

    Figure 1.  Faster R-CNN detection flowchart

    图 2  锚点框示意图

    Figure 2.  Schematic diagram of anchor box

    图 3  VGG16、ResNet101结构示意图

    Figure 3.  Schematic diagram of VGG16 and ResNet101 structure

    图 4  bottleneck结构示意图

    Figure 4.  Schematic diagram of bottleneck structure

    图 5  改进的Faster R-CNN结构

    Figure 5.  Structure of improved Faster R-CNN

    图 6  预设锚点框范围示例

    Figure 6.  Example range of preset anchor box

    图 7  RoI池化、RoI Align示意图

    Figure 7.  Schematic diagram of RoI Pooling and RoI Align

    图 8  VOC数据集中各目标图像数量

    Figure 8.  Number of various target images of VOC dataset

    图 9  飞机目标信息统计直方图

    Figure 9.  Statistical histogram of aircraft target information

    图 10  SAD数据集中部分SAR飞机图像

    Figure 10.  Some SAR aircraft images in SAD dataset

    图 11  不同算法P-R曲线比较

    Figure 11.  P-R curves comparison among different algorithms

    图 12  检测结果对比

    Figure 12.  Comparison of detection results

    表  1  飞机目标统计参数

    Table  1.   Statistical parameters of aircraft target

    统计参数 最大值 最小值 平均值
    宽度/像素 459 27 87
    宽度占比 0.510 0.030 0.097
    高度/像素 378 42 81
    高度占比 0.630 0.070 0.135
    面积/像素 173 502 1 134 7 047
    面积占比 0.321 0.002 0.013
    下载: 导出CSV

    表  2  实验结果对比

    Table  2.   Comparison of experimental results

    算法
    特征提取
    网络
    AP/% R/% P/% 检测速度/
    fps
    Faster R-CNN VGG16 81.1 78.1 77.0 16.6
    Faster R-CNN ResNet101 82.3 80.2 79.4 13.2
    Faster R-CNN+
    k-means
    ResNet101 83.5 81.7 80.8 13.5
    本文算法 ResNet101 88.5 89.5 85.9 12.7
    注:fps为帧/s。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-04
  • 录用日期:  2020-05-22
  • 网络出版日期:  2021-01-20

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