结合强化学习自适应候选框挑选的SAR目标检测方法

杜兰 王梓霖 郭昱辰 杜宇昂 严俊坤

杜兰, 王梓霖, 郭昱辰, 等. 结合强化学习自适应候选框挑选的SAR目标检测方法[J]. 雷达学报, 2022, 11(5): 884–896. doi: 10.12000/JR22121
引用本文: 杜兰, 王梓霖, 郭昱辰, 等. 结合强化学习自适应候选框挑选的SAR目标检测方法[J]. 雷达学报, 2022, 11(5): 884–896. doi: 10.12000/JR22121
DU Lan, WANG Zilin, GUO Yuchen, et al. Adaptive region proposal selection for SAR target detection using reinforcement learning[J]. Journal of Radars, 2022, 11(5): 884–896. doi: 10.12000/JR22121
Citation: DU Lan, WANG Zilin, GUO Yuchen, et al. Adaptive region proposal selection for SAR target detection using reinforcement learning[J]. Journal of Radars, 2022, 11(5): 884–896. doi: 10.12000/JR22121

结合强化学习自适应候选框挑选的SAR目标检测方法

doi: 10.12000/JR22121
基金项目: 国家自然科学基金(U21B2039)
详细信息
    作者简介:

    杜 兰,博士,教授,主要研究方向为雷达目标识别、雷达信号处理、机器学习

    王梓霖,硕士生,主要研究方向为SAR图像目标检测、机器学习等

    郭昱辰,博士,讲师,主要研究方向为智能雷达目标检测和识别

    杜宇昂,博士生,主要研究方向为SAR图像解译、机器学习与人工智能等

    严俊坤,博士,教授,主要研究方向为单站雷达智能信号处理和网络化雷达协同探测

    通讯作者:

    杜兰 dulan@mail.xidian.edu.cn

  • 责任主编:徐丰 Corresponding Editor: XU Feng
  • 中图分类号: TN957.51

Adaptive Region Proposal Selection for SAR Target Detection Using Reinforcement Learning

Funds: The National Natural Science Foundation of China (U21B2039)
More Information
  • 摘要: 大场景合成孔径雷达(SAR)图像相对于通用光学图像,复杂背景杂波对目标特征提取影响更大,由于传统基于候选框的深度目标检测算法会在整张特征图上产生大量冗余候选框,因而在SAR图像复杂背景杂波影响下会产生大量的虚警,降低目标检测精度。针对该问题,该文基于Faster R-CNN检测模型,提出结合强化学习自适应候选框挑选的SAR目标检测方法。该方法能够通过强化学习自适应搜索特征图中可能含有目标的区域,并挑选搜索区域内的候选框继续进行分类、回归。通过准确搜索到含有目标的区域,可以减少复杂背景杂波的影响并减少传统强化学习应用于检测问题的计算量。所提方法利用强化学习序列决策的特点,能够根据图像信息通过强化学习迭代搜索自适应确定图像中可能含有目标的搜索区域的位置。同时,该方法通过在强化学习中使用距离约束,可以根据之前的搜索结果自适应调整下一次搜索区域的尺寸。基于实测数据的实验结果表明,所提方法能够提升传统深度学习目标检测方法的检测性能。

     

  • 图  1  Faster R-CNN结构

    Figure  1.  Faster R-CNN structure

    图  2  强化学习原理

    Figure  2.  Principles of reinforcement learning

    图  3  结合强化学习的SAR目标检测方法整体框架

    Figure  3.  Framework of SAR target detection method using reinforcement learning

    图  4  ROC曲线对比分析

    Figure  4.  ROC curves comparative analysis

    5  SAR图像目标检测结果

    5.  Detection results of SAR images

    图  6  训练图像样本示例(白色框为固定动作所产生的搜索区域)

    Figure  6.  Training image example (The white box indicates the search area generated by fixed action)

    图  7  可视化搜索过程(白色框为固定动作所产生的搜索区域)

    Figure  7.  Visualization of search (The white box indicates the search area generated by fixed action)

    图  8  RoI分布对比(白色框为固定动作所产生的搜索区域)

    Figure  8.  Visualization of RoI (The white box indicates the search area generated by fixed action)

    图  9  运行时间与F1-score关系

    Figure  9.  Runtime versus F1-score

    算法 1 自适应候选框挑选方法
    Alg. 1 Adaptive region proposal selection
     输入:状态${s_t} = $$\{ {R_t},{S_t},{H_t}\} $,最大迭代次数为$M = 10$
     输出:搜索区域中心坐标和范围参数$\{ {z_t},{p_t}\} $
     1:初始化状态量${s_t} = \{ {R_{t - 1}},{S_{t - 1}},{H_t}\} $
     2:for $t = 1 \to M$ do //t表示迭代次数
     3:  if $(\pi (a_t^d = 1|{s_t}) > \eta )$ then //$ \eta $为概率阈值,取0.5
     4:    break //结束迭代搜索
     5:  else
     6:    ${z_t} \Leftarrow \max \pi (a_t^d = 0,{\text{ }}a_t^f = {z_t}|{s_t})$
     7:    ${p_t} \Leftarrow {z_t},{z_{t - 1}}$ //计算搜索区域中心坐标和范围
     8:  end if
     9:  输出${z_t},{p_t}$ // 挑选候选框并送入后续检测器部分进行检测
     10:  ${R_t} \Leftarrow {R_{t - 1}},{\text{ }}{S_t} \Leftarrow {S_{t - 1}}$ //更新状态${R_t},{\text{ }}{S_t}$
     11:end for
    下载: 导出CSV

    表  1  不同方法实验结果

    Table  1.   Experimental results of different methods

    方法PRF1-score
    Gaussian-CFAR0.37890.79660.5135
    Faster R-CNN0.81560.92680.8677
    SSD0.84510.91060.8750
    Faster R-CNN+CBAM0.84090.92680.8818
    本文方法-尺寸固定0.83690.93500.8832
    本文方法0.86860.93500.9006
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-22
  • 修回日期:  2022-08-24
  • 网络出版日期:  2022-09-02
  • 刊出日期:  2022-10-28

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