Submit Manuscript
Peer Review
Editor Work
| 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, 2024, 13(6): 1184–1201. doi: 10.12000/JR24163
|
Three-dimensional Reconstruction Method for Detecting Small Targets within Walls Based on a Multistage Cascade U-Net Approach Using Ground Penetrating Radars
DOI: 10.12000/JR24163
More Information-
Abstract
Ground Penetrating radars (GPR) are essential for detecting buried targets in civilian and military applications, especially given the increasing demand for detecting and imaging small targets within walls. The complex structures and materials of walls pose substantial challenges for precisely reconstructing small targets. To address this issue, this study proposes a multistage cascaded U-Net approach for the three-dimensional reconstruction of small targets within walls. First, we developed a high-resolution detection model and a dataset tailored to handle complex wall scenes. Thereafter, using the Monte Carlo sampling method, we sampled aggregate particle sizes to create a physical three-dimensional aggregate scattering model that satisfies grading requirements, thus enhancing the realism and accuracy of the simulated scenes. Our multistage network design effectively suppresses noise and inhomogeneous clutter in C-scan data, thereby improving signal quality. The preprocessed data are then fed into subsequent network stages to reconstruct the distribution of three-dimensional reconstruction values. In addition, we proposed an adaptive multiscale module and a cascaded network training strategy to better fit small target information in complex scenes. Through comparisons with simulated and measured data, we confirmed the effectiveness and generalizability of our method. Unlike existing techniques, our approach successfully reconstructs small targets within three-dimensional walls, thereby considerably enhancing the peak signal-to-noise ratio and providing critical technical support for accurately detecting small targets within walls. -
-
References
[1] ZHANG Ju, HU Qingwu, ZHOU Yemei, et al. A multi-level robust positioning method for three-dimensional ground penetrating radar (3D GPR) road underground imaging in dense urban areas[J]. Remote Sensing, 2024, 16(9): 1559. doi: 10.3390/rs16091559.[2] DANIELS D J. A review of GPR for landmine detection[J]. Sensing and Imaging: An International Journal, 2006, 7(3): 90–123. doi: 10.1007/s11220-006-0024-5.[3] XUE Fangxiu, ZHANG Xiaowei, WANG Zepeng, et al. Analysis of imaging internal defects in living trees on irregular contours of tree trunks using ground-penetrating radar[J]. Forests, 2021, 12(8): 1012. doi: 10.3390/f12081012.[4] ZHOU Huawei, HU Hao, ZOU Zhihui, et al. Reverse time migration: A prospect of seismic imaging methodology[J]. Earth-Science Reviews, 2018, 179: 207–227. doi: 10.1016/j.earscirev.2018.02.008.[5] ZOU Lilong, WANG Yan, GIANNAKIS I, et al. Mapping and assessment of tree roots using ground penetrating radar with low-cost GPS[J]. Remote Sensing, 2020, 12(8): 1300. doi: 10.3390/rs12081300.[6] WEN Jian, LI Zhaoxi, and XIAO Jiang. Noise removal in tree radar B-scan images based on shearlet[J]. Wood Research, 2020, 65(1): 1–12. doi: 10.37763/wr.1336-4561/65.1.001012.[7] FENG Deshan, DING Siyuan, WANG Xun, et al. Wavefield reconstruction inversion of GPR data for permittivity and conductivity models in the frequency domain based on modified total variation regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5903314. doi: 10.1109/TGRS.2021.3077476.[8] KLOTZSCHE A, VEREECKEN H, and VAN DER KRUK J. Review of crosshole ground-penetrating radar full-waveform inversion of experimental data: Recent developments, challenges, and pitfalls[J]. Geophysics, 2019, 84(6): H13–H28. doi: 10.1190/geo2018-0597.1.[9] LIU Yuxin, FENG Deshan, XIAO Yougan, et al. Full-waveform inversion of multifrequency GPR data using a multiscale approach based on deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 60: 5910212. doi: 10.1109/TGRS.2024.3382331.[10] FENG Deshan, LI Bingchao, WANG Xun, et al. An efficient dual-parameter full waveform inversion for GPR data using data encoding[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5920011. doi: 10.1109/TGRS.2023.3321059.[11] 雷林林, 刘四新, 傅磊, 等. 基于全波形反演的探地雷达数据逆时偏移成像[J]. 地球物理学报, 2015, 58(9): 3346–3355. doi: 10.6038/cjg20150927.LEI Linlin, LIU Sixin, FU Lei, et al. Reverse time migration applied to GPR data based on full wave inversion[J]. Chinese Journal of Geophysics, 2015, 58(9): 3346–3355. doi: 10.6038/cjg20150927.[12] WANG Xun and FENG Deshan. Multiparameter full-waveform inversion of 3-D on-ground GPR with a modified total variation regularization scheme[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(3): 466–470. doi: 10.1109/LGRS.2020.2976146.[13] 国运东, 孟凡冰, 秦广胜, 等. 基于动态平面波的三维多尺度全波形反演方法[J]. 石油物探, 2022, 61(4): 616–624. doi: 10.3969/j.issn.1000-1441.2022.04.005.GUO Yundong, MENG Fanbing, QIN Guangsheng, et al. 3D multi-scale full waveform inversion based on the dynamic plane-wave encoding method[J]. Geophysical Prospecting for Petroleum, 2022, 61(4): 616–624. doi: 10.3969/j.issn.1000-1441.2022.04.005.[14] DENG Jian, RENDERS J, MEENAKETAN B L P, et al. Imaging algorithm and optimal measurement protocol for 3-D impedance tomography on 2-D high-density microelectrode array[J]. IEEE Sensors Journal, 2024, 24(15): 24452–24465. doi: 10.1109/JSEN.2024.3417289.[15] LI Shucai, LIU Bin, REN Yuxiao, et al. Deep-learning inversion of seismic data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3): 2135–2149. doi: 10.1109/TGRS.2019.2953473.[16] LIU Bin, REN Yuxiao, LIU Hanchi, et al. GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(10): 8305–8325. doi: 10.1109/TGRS.2020.3046454.[17] JI Yintao, ZHANG Fengkai, WANG Jing, et al. Deep neural network-based permittivity inversions for ground penetrating radar data[J]. IEEE Sensors Journal, 2021, 21(6): 8172–8183. doi: 10.1109/JSEN.2021.3050618.[18] SUN Haoran, YANG Xiaopeng, GONG Junbo, et al. Joint physics and data driven full-waveform inversion for underground dielectric targets imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4513311. doi: 10.1109/TGRS.2022.3219138.[19] DAI Qiqi, LEE Y H, SUN Haihan, et al. DMRF-UNet: A two-stage deep learning scheme for GPR data inversion under heterogeneous soil conditions[J]. IEEE Transactions on Antennas and Propagation, 2022, 70(8): 6313–6328. doi: 10.1109/TAP.2022.3176386.[20] WANG Mengjun, HU Da, CHEN Junjie, et al. Underground infrastructure detection and localization using deep learning enabled radargram inversion and vision based mapping[J]. Automation in Construction, 2023, 154: 105004. doi: 10.1016/j.autcon.2023.105004.[21] XIE Longhao, ZHAO Qing, MA Chunguang, et al. Ü-Net: Deep-learning schemes for ground penetrating radar data inversion[J]. Journal of Environmental and Engineering Geophysics, 2020, 25(2): 287–292. doi: 10.2113/JEEG19-074.[22] PANDA S L, SAHOO U K, MAITI S, et al. An attention U-Net-based improved clutter suppression in GPR images[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 8502511. doi: 10.1109/TIM.2024.3378267.[23] GE Junkai, SUN Huaifeng, SHAO Wei, et al. GPR-TransUNet: An improved transunet based on self-attention mechanism for ground penetrating radar inversion[J]. Journal of Applied Geophysics, 2024, 222: 105333. doi: 10.1016/j.jappgeo.2024.105333.[24] DAI Qiqi, LEE Y H, SUN Haihan, et al. 3DInvNet: A deep learning-based 3D ground-penetrating radar data inversion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5105016. doi: 10.1109/TGRS.2023.3275306.[25] XU Tianjia, YUAN Da, WANG Ping, et al. Improved 3-D representation of GPR pipelines B-scan sequences using a neural network framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 4502416. doi: 10.1109/TGRS.2024.3360101.[26] ZOUBIR A M, CHANT I J, BROWN C L, et al. Signal processing techniques for landmine detection using impulse ground penetrating radar[J]. IEEE Sensors Journal, 2002, 2(1): 41–51. doi: 10.1109/7361.987060.[27] XUE Wei, LUO Yan, YANG Yue, et al. Noise suppression for GPR data based on SVD of window-length-optimized hankel matrix[J]. Sensors, 2019, 19(17): 3807. doi: 10.3390/s19173807.[28] BI Wenda, ZHAO Yonghui, AN Cong, et al. Clutter elimination and random-noise denoising of GPR signals using an svd method based on the hankel matrix in the local frequency domain[J]. Sensors, 2018, 18(10): 3422. doi: 10.3390/s18103422.[29] KUMLU D and ERER I. Improved clutter removal in GPR by robust nonnegative matrix factorization[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(6): 958–962. doi: 10.1109/LGRS.2019.2937749.[30] SONG Xiaoji, XIANG Deliang, ZHOU Kai, et al. Improving RPCA-based clutter suppression in GPR detection of antipersonnel mines[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1338–1342. doi: 10.1109/LGRS.2017.2711251.[31] NI Zhikang, YE Shengbo, SHI Cheng, et al. Clutter suppression in GPR B-scan images using robust autoencoder[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3500705. doi: 10.1109/LGRS.2020.3026007.[32] CAO Yanjie, YANG Xiaopeng, GUO Conglong, et al. Subspace projection attention network for GPR heterogeneous clutter removal[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 3917–3926. doi: 10.1109/JSTARS.2024.3355213.[33] NI Zhikang, SHI Cheng, PAN Jun, et al. Declutter-GAN: GPR B-scan data clutter removal using conditional generative adversarial nets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4023105. doi: 10.1109/LGRS.2022.3159788.[34] KAYACAN Y E and ERER I. A vision-transformer-based approach to clutter removal in GPR: DC-ViT[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3505105. doi: 10.1109/LGRS.2024.3385694.[35] ZHANG Yan, DIAO Enmao, HUSTON D, et al. A data-efficient deep learning method for rough surface clutter reduction in GPR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5104610. doi: 10.1109/TGRS.2024.3382545.[36] LI Boyang, YUAN Da, YANG Gexing, et al. Flexibility-residual BiSeNetV2 for GPR image decluttering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5106812. doi: 10.1109/TGRS.2023.3296722.[37] WARREN C, GIANNOPOULOS A, and GIANNAKIS I. gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar[J]. Computer Physics Communications, 2016, 209: 163–170. doi: 10.1016/j.cpc.2016.08.020. -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Multistage cascade U-Net network structure diagram
- Figure 2. Schematic diagram of the adaptive multiscale module structure
- Figure 3. Illustration of the three-dimensional aggregate non-uniform wall model
- Figure 4. 2D slice of a 3D non-uniform model
- 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)
- Figure 6. Slice comparison of denoising effects under different methods (slices 1 and 2 are examples selected from the denoising results of example 2)
- 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)
- Figure 8. Denoising results of the proposed scheme on four different test sets
- Figure 9. Reconstruction results of the proposed scheme on four different test sets
- 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)
- Figure 11. Reconstruction results of five experiments on the test set (examples 11 and 12 are randomly selected examples from the reconstruction results of the test set)
- Figure 12. Experimental setup for collecting actual measurement data
- Figure 13. Reconstruction results of the proposed method on measured data example 13
- Figure 14. Reconstruction results of the proposed method on measured data example 14