探地雷达多阶段级联 U-Net 墙内小目标三维重建方法

兰天 盛世文 孙熙韬 高炜程 杨小鹏

兰天, 盛世文, 孙熙韬, 等. 探地雷达多阶段级联 U-Net 墙内小目标三维重建方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24163
引用本文: 兰天, 盛世文, 孙熙韬, 等. 探地雷达多阶段级联 U-Net 墙内小目标三维重建方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24163
LAN Tian, SHENG Shiwen, SUN Xitao, et al. Three-dimensional reconstruction method for detecting small targets within walls based on a multistage cascade U-Net approach using ground-penetrating radars[J]. Journal of Radars, in press. doi: 10.12000/JR24163
Citation: LAN Tian, SHENG Shiwen, SUN Xitao, et al. Three-dimensional reconstruction method for detecting small targets within walls based on a multistage cascade U-Net approach using ground-penetrating radars[J]. Journal of Radars, in press. doi: 10.12000/JR24163

探地雷达多阶段级联 U-Net 墙内小目标三维重建方法

DOI: 10.12000/JR24163
基金项目: 国家自然科学基金(62471037, 62101042),中央高校基本科研业务费专项资金(XSQD-6120220083)
详细信息
    作者简介:

    兰 天,博士,硕士生导师,主要研究方向为雷达信号处理、电磁逆散射

    盛世文,硕士生,主要研究方向为探地雷达介电常数反演、深度学习

    孙熙韬,硕士生,主要研究方向为探地雷达三维偏移成像与检测

    高炜程,博士生,主要研究方向为信号处理中的数学原理及建模理论、穿墙雷达人体行为及步态识别技术

    杨小鹏,博士,博士生导师,主要研究方向为穿墙雷达、探地雷达、相控阵雷达和自适应阵列信号处理

    通讯作者:

    杨小鹏 xiaopengyang@bit.edu.cn

  • 责任主编:郭世盛 Corresponding Editor: GUO Shisheng
  • 中图分类号: TN959

Three-dimensional Reconstruction Method for Detecting Small Targets within Walls based on a Multistage Cascade U-Net Approach Using Ground-penetrating Radars

Funds: The National Natural Science Foundation of China (62471037, 62101042), Special Fund for Basic Scientific Research Operations of Central Universities (XSQD-6120220083)
More Information
  • 摘要: 探地雷达(GPR)在对掩埋目标的探测中发挥着至关重要的作用,尤其在墙体内小目标检测及重建方面。由于墙体结构及材质的复杂性,墙内小目标精准重建面临极大挑战。针对墙内小目标重建难题,该文提出了一种多阶段级联 U-Net 方法,用于墙内小目标的三维重建。首先,通过蒙特卡罗抽样生成符合级配要求的物理三维骨料散射模型,构建了复杂墙体场景的高分辨率探测模型和数据集,以提高模拟的真实性和准确性;其次,多阶段网络结构的设计能够有效抑制 C 扫描数据中的噪声和非均质杂波,从而提升信号质量;最后,预处理后的数据用于重建小目标三维分布。此外,该文还引入了一种自适应多尺度模块和级联网络训练策略,优化了复杂场景中小目标信息的拟合性能。通过模拟与实测数据的对比,验证所提方法的有效性和泛化能力。相比现有技术,该方法成功重建了三维墙体内小目标,显著提高了峰值信噪比,为小目标的准确探测提供了重要技术支持。

     

  • 图  1  多阶段级联U-Net网络结构图

    Figure  1.  Multi-stage cascade U-Net network structure diagram

    图  2  自适应多尺度模块结构示意图

    Figure  2.  Schematic diagram of the adaptive multi-scale module structure

    图  3  三维骨料非均匀墙体模型的图示

    Figure  3.  Illustration of the three-dimensional aggregate non-uniform wall model

    图  4  三维非均匀模型的二维切片

    Figure  4.  2D slice of a 3D non-uniform model

    图  5  不同方法下的降噪效果对比图。算例1至2是从测试集的降噪结果中随机选择的示例

    Figure  5.  Comparison of denoising effects under different methods. Examples 1 to 2 are randomly selected examples from the denoising results of the test set

    图  6  不同方法下的降噪效果切片对比图。切片1和2是从算例2的降噪结果中选择的示例

    Figure  6.  Slice comparison of denoising effects under different methods. Slices 1 and 2 are examples selected from the denoising results of Example 2

    图  7  所提方法下的重建效果对比图。算例1 至4 是从测试集的重建结果中随机选择的示例

    Figure  7.  Comparison of reconstruction results of the proposed method. Examples 1 to 4 are randomly selected examples from the reconstruction results of the test set

    图  8  所提方法在4个不同测试集上的降噪结果

    Figure  8.  Denoising results of the proposed scheme on four different test sets

    图  9  所提方法在4个不同测试集上的重建结果

    Figure  9.  Reconstruction results of the proposed scheme on four different test sets

    图  10  所提方法在不同背景材质测试集上的重建结果。算例9到10是从测试集的重建结果中随机选择的示例

    Figure  10.  Reconstruction results of the proposed method on the test set with different background materials. Examples 9 to 10 are randomly selected examples from the reconstruction results of the test set

    图  11  5种情况在测试集上的重建结果,算例11和算例12是从测试集的重建结果中随机选择的示例

    Figure  11.  Reconstruction results of five situations on the test set. Examples 11 and 12 are randomly selected examples from the reconstruction results of the test set

    图  12  用于采集实际测量数据的实验装置

    Figure  12.  Experimental setup for collecting actual measurement data

    图  13  所提方法在实测数据算例13上的重建结果

    Figure  13.  Reconstruction results of the proposed method on measured data example 13

    图  14  所提方法在实测算例14上的重建结果

    Figure  14.  Reconstruction results of the proposed method on measured data example 14

    表  1  仿真数据集预设置参数

    Table  1.   Simulation data set preset parameters

    目标参数 设置范围
    数量 1~2
    形状 圆柱体和长方体
    位置 随机
    介质类型 金属
    掩埋深度 40~60 mm
    圆柱体厚度 10~20 mm
    圆柱体半径 10~20 mm
    长方体长、宽、高 15~30 mm
    下载: 导出CSV

    表  2  不同方法下的降噪评估指标对比

    Table  2.   Comparison of denoising evaluation indicators under different methods

    变量PSNR↑/(dB)RMSE (↓)MAE (↓)
    方法\骨科配级比(%)246810246810246810
    RNMF33.2134.4333.633.3533.940.53550.49130.58360.4580.56360.09290.08590.09370.08810.0905
    RPCA38.2239.0538.538.3638.930.29920.28810.33730.2560.31580.07610.07090.07730.07220.0741
    3DInvNet[24]35.9237.1336.235.936.520.39310.36110.43510.3380.41990.04290.03750.04320.04120.0418
    所提方法56.1856.95553.0154.620.03680.03570.04720.0480.04950.01830.01680.01960.02010.0205
    下载: 导出CSV

    表  3  不同方法下的重建评估指标对比

    Table  3.   Comparison of reconstruction evaluation indicators under different methods

    指标 3DInvNet[24] 所提方法
    PSNR↑/(dB) 34.78 52.17
    SSIM↑ 0.9969 0.9986
    RMSE↓ 0.1629 0.0074
    MAE↓ 0.0092 0.0071
    OR 0 0.9241
    AR 0 4.0985
    均值 0.0001 17.5678
    下载: 导出CSV

    表  4  所提方法在不同测试集上的评估指标

    Table  4.   Evaluation indicators of the proposed scheme on different test sets

    测试集 PSNR↑/(dB) SSIM↑ RMSE↓ MAE↓
    改变
    骨料模型
    48.63 0.9974 0.0081 0.0115
    增加
    旋转目标
    46.74 0.9978 0.0209 0.0101
    增加
    目标数量
    36.83 0.9945 0.0983 0.0199
    增加
    骨料含量
    43.77 0.9962 0.0309 0.1443
    下载: 导出CSV

    表  5  所提方法在不同背景材质下的重建指标对比

    Table  5.   Comparison of reconstruction indicators of the proposed scheme under different background materials

    背景介电常数值 PSNR↑/(dB) SSIM↑ RMSE↓ MAE↓
    5.5 42.15 0.9945 0.0338 0.0161
    5.75 45.70 0.9969 0.0157 0.0124
    6.0 48.63 0.9974 0.0086 0.0115
    6.25 48.14 0.9968 0.0086 0.0123
    6.5 45.10 0.9949 0.0159 0.0151
    下载: 导出CSV

    表  6  消融实验

    Table  6.   Ablation experiment

    实验\指标 PSNR↑/(dB) SSIM↑ RMSE↓ MAE↓
    (1) 44.85 0.9976 0.0219 0.0108
    (2) 50.34 0.9976 0.0129 0.0079
    (3) 31.07 0.6391 0.3735 0.2388
    (4) 51.29 0.9981 0.0081 0.0097
    (5) 52.17 0.9986 0.0074 0.0071
    下载: 导出CSV

    表  7  所提方法在实测算例的重建评估指标对比

    Table  7.   Comparison of reconstruction evaluation indicators of the proposed method in the measured example

    指标 数值
    PSNR↑/(dB) 36.49
    SSIM↑ 0.9805
    RMSE↓ 0.1033
    MAE↓ 0.04155
    OR 0.5708
    AR 4.5737
    均值 13.5055
    下载: 导出CSV
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  • 收稿日期:  2024-08-15
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