基于特征转移金字塔网络的SAR图像跨尺度目标检测

周正 崔宗勇 曹宗杰 杨建宇

周正, 崔宗勇, 曹宗杰, 等. 基于特征转移金字塔网络的SAR图像跨尺度目标检测[J]. 雷达学报, 2021, 10(4): 544–558. doi: 10.12000/JR21059
引用本文: 周正, 崔宗勇, 曹宗杰, 等. 基于特征转移金字塔网络的SAR图像跨尺度目标检测[J]. 雷达学报, 2021, 10(4): 544–558. doi: 10.12000/JR21059
ZHOU Zheng, CUI Zongyong, CAO Zongjie, et al. Feature-transferable pyramid network for cross-scale object detection in SAR images[J]. Journal of Radars, 2021, 10(4): 544–558. doi: 10.12000/JR21059
Citation: ZHOU Zheng, CUI Zongyong, CAO Zongjie, et al. Feature-transferable pyramid network for cross-scale object detection in SAR images[J]. Journal of Radars, 2021, 10(4): 544–558. doi: 10.12000/JR21059

基于特征转移金字塔网络的SAR图像跨尺度目标检测

doi: 10.12000/JR21059
基金项目: 国家自然科学基金(61971101, 61801098),自动目标识别国家重点实验室基金(6142503190201)
详细信息
    作者简介:

    周正:周 正(1995–),男,四川眉山人,电子科技大学信息与通信工程学院在读博士研究生,主要研究方向为SAR目标检测识别等

    崔宗勇(1984–),男,山东菏泽人,电子科技大学信息与通信工程学院副教授,主要研究方向为SAR图像处理、目标识别、深度学习等

    曹宗杰(1977–),男,山西太谷人,电子科技大学信息与通信工程学院教授,主要研究方向为SAR目标检测识别、图像处理、人工智能等

    杨建宇(1963–),男,电子科技大学教授,博士生导师,主要研究方向为雷达前视成像、实孔径超分辨成像、双多基合成孔径雷达成像。获国家出版基金资助出版专著1部。获省部级奖6项、国家技术发明二等奖2项

    通讯作者:

    崔宗勇 zycui@uestc.edu.cn

  • 责任主编:计科峰 Corresponding Editor: JI Kefeng
  • 中图分类号: TN959.72

Feature-transferable Pyramid Network for Cross-scale Object Detection in SAR Images

Funds: The National Natural Science Foundation of China (61971101, 61801098), Science and Technology on Automatic Target Recognition Laboratory (ATR) Fund (6142503190201)
More Information
  • 摘要: SAR图像多尺度目标检测能够实现大场景SAR图像中关键目标的定位与识别,是SAR图像解译的关键技术之一。然而针对尺寸相差较大的SAR目标的同时检测,即跨尺度目标检测问题,现有目标检测方法难以实现。该文提出一种基于特征转移金字塔网络(FTPN)的SAR图像跨尺度目标检测方法。在特征提取阶段采用特征转移方法,实现各层特征图的有效连接,实现不同尺度特征图的提取;同时采用空洞卷积群方法,增大特征提取的感受野,促使网络提取到大尺度目标特征。上述环节能够有效保留不同尺寸目标特征,从而实现SAR图像中跨尺度目标的同时检测。基于高分三号SAR数据、SSDD数据集及高分辨率SAR舰船检测数据集-2.0等数据集的试验表明,该文方法能够实现SAR图像中机场、舰船等跨尺度目标的检测,在已有数据集上mAP达96.5%,较特征金字塔网络算法提升8.1%,并且整体性能优于现阶段最新的YOLOv4等目标检测算法。

     

  • 图  1  跨尺度目标

    Figure  1.  Cross-scale objects

    图  2  特征转移网络结构

    Figure  2.  Feature-transferrable network structure

    图  3  空洞卷积群

    Figure  3.  Dilated convolution group

    图  4  FTPN的框架

    Figure  4.  Framework of FTPN

    图  5  特征转移层对检测结果的影响

    Figure  5.  Influence of feature-transfer layer on detection results

    图  6  空洞卷积群对检测结果的影响

    Figure  6.  Influence of dilated convolution group on detection results

    图  7  不同感受野的检测结果

    Figure  7.  Detection results of different receptive fields

    图  8  与其他方法的比较

    Figure  8.  Comparison with other methods

    图  9  大场景SAR图像跨尺度目标检测结果

    Figure  9.  Cross-scale object detection results in large scene SAR images

    图  10  不同尺度比的检测结果

    Figure  10.  Detection results of different scale ratios

    图  11  不同单一尺度的检测结果

    Figure  11.  Different single scale detection results

    表  1  特征转移层对检测结果的影响

    Table  1.   The influence of feature-transfer layer on detection results

    方法mAP(%)
    没有特征转移层88.4
    本文方法92.8
    下载: 导出CSV

    表  2  空洞卷积群对检测结果的影响

    Table  2.   Influence of dilated convolution group on detection results

    方法mAP(%)
    没有空洞卷积群88.4
    本文方法>92.1
    下载: 导出CSV

    表  3  与先进的目标检测网络相比

    Table  3.   Compared with advanced object detection networks

    方法mAP(%)
    Faster R-CNN70.1
    SSD78.5
    YOLOv488.2
    YOLOv588.5
    Improved Faster R-CNN88.8
    DAPN89.8
    PANet90.8
    SGE-centernet93.9
    本文方法96.5
    下载: 导出CSV

    表  4  机场目标和舰船目标的结果统计

    Table  4.   Result statistics for airport and ship objects

    目标类型NnmfDP(%)MP(%)FP(%)
    机场330010000
    舰船8076459556.25
    下载: 导出CSV

    表  5  单一尺度目标的检测性能

    Table  5.   Single scale object detection performance

    单一尺度mAP(%)
    小尺度舰船97.2
    大尺度舰船96.3
    大尺度机场94.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-06
  • 修回日期:  2021-07-14
  • 网络出版日期:  2021-07-29
  • 刊出日期:  2021-08-28

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