A Review of Target Detection Algorithm for GPR B-scan Processing

Feifei HOU; Ronghua SHI; Wentai LEI; Jian DONG; Mengdi XU; Jingchun XI

doi:10.11999/JEIT190680

Volume 42 Issue 1

Jan. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(1): 191-200

Feifei HOU, Ronghua SHI, Wentai LEI, Jian DONG, Mengdi XU, Jingchun XI. A Review of Target Detection Algorithm for GPR B-scan Processing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 191-200. doi: 10.11999/JEIT190680

Citation:

Feifei HOU, Ronghua SHI, Wentai LEI, Jian DONG, Mengdi XU, Jingchun XI. A Review of Target Detection Algorithm for GPR B-scan Processing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 191-200. doi: 10.11999/JEIT190680

Citation:

PDF( 1782 KB)

A Review of Target Detection Algorithm for GPR B-scan Processing

doi: 10.11999/JEIT190680

Department of Computer Science and Engineering, Central South University, Changsha 410000, China

Funds: The National Natural Science Foundation of China (61102139, 61872390), The Fundamental Rresearch Funds for the Central Universities of Central South University (2018zzts181)

Received Date: 2019-09-04
Rev Recd Date: 2019-11-12

Available Online: 2019-11-18

Publish Date: 2020-01-21

Abstract

Abstract

Ground Penetrating Radar (GPR), as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discusses the feasibility of using GPR to estimate target positions, and reviews the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.
- Ground Penetrating Radar (GPR),
- Underground target detection,
- Machine Learning (ML),
- Deep Learning (DL),
- Hyperbolic reflection

FullText(HTML)

References(60)

References

JOL H M. 雷文太, 童孝忠, 周旸, 译. 探地雷达理论与应用[M]. 北京: 电子工业出版社, 2011.

JOL H M. LEI Wentai, TONG Xiaozhong, ZHOU Yang, translation. Ground Penetrating Radar: Theory and Applications[M]. Beijing: Publishing House of Electronics Industry, 2011.

BESAW L E and STIMAC P J. Deep convolutional neural networks for classifying GPR B-Scans[J]. SPIE, 2015, 9454: 945413.

BESAW L E. Detecting buried explosive hazards with handheld GPR and deep learning[J]. SPIE, 2016, 9823: 98230N. doi: 10.1117/12.2223797

BRALICH J, REICHMAN D, COLLINS L M, et al. Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining[J]. SPIE, 2017: 10182.

REICHMAN D, COLLINS L M, and MALOF J M. Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar[C]. The 9th International Workshop on Advanced Ground Penetrating Radar, Edinburgh, UK, 2017: 1–5.

LAMERI S, LOMBARDI F, BESTAGINI P, et al. Landmine detection from GPR data using convolutional neural networks[C]. The 25th European Signal Processing Conference, Kos, Greece, 2017: 508–512.

BENEDETTO A, BENEDETTO F, DE BLASⅡS M R, et al. Reliability of radar inspection for detection of pavement damage[J]. Road Materials and Pavement Design, 2004, 5(1): 93–110. doi: 10.1080/14680629.2004.9689964

LEI Wentai, SHI Ronghua, DONG Jian, et al. A multi-scale weighted back projection imaging technique for ground penetrating radar applications[J]. Remote Sensing, 2014, 6(6): 5151–5163. doi: 10.3390/rs6065151

LEI Wentai, ZENG Sheng, ZHAO Jian, et al. An improved back projection imaging algorithm for subsurface target detection[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2013, 21(6): 1820–1826.

BENEDETTO F and TOSTI F. GPR spectral analysis for clay content evaluation by the frequency shift method[J]. Journal of Applied Geophysics, 2013, 97: 89–96. doi: 10.1016/j.jappgeo.2013.03.012

KAUR P, DANA K J, ROMERO F A, et al. Automated GPR rebar analysis for robotic bridge deck evaluation[J]. IEEE Transactions on Cybernetics, 2016, 46(10): 2265–2276. doi: 10.1109/TCYB.2015.2474747

DINH K, GUCUNSKI N, and DUONG T H. An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks[J]. Automation in Construction, 2018, 89: 292–298. doi: 10.1016/j.autcon.2018.02.017

YUAN Chenxi, LI Shuai, CAI Hubo, et al. GPR signature detection and decomposition for mapping buried utilities with complex spatial configuration[J]. Journal of Computing in Civil Engineering, 2018, 32(4): 04018026. doi: 10.1061/(ASCE)CP.1943-5487.0000764

LI Shuai, CAI Hubo, and KAMAT V R. Uncertainty-aware geospatial system for mapping and visualizing underground utilities[J]. Automation in Construction, 2015, 53: 105–119. doi: 10.1016/j.autcon.2015.03.011

LI Shuai, CAI Hubo, ABRAHAM D M, et al. Estimating features of underground utilities: Hybrid GPR/GPS approach[J]. Journal of Computing in Civil Engineering, 2016, 30(1): 04014108. doi: 10.1061/(ASCE)CP.1943-5487.0000443

ILLINGWORTH J and KITTLER J. A survey of the Hough transform[J]. Computer Vision, Graphics, and Image Processing, 1988, 43(2): 280.

BORGIOLI G, CAPINERI L, FALORNI P, et al. The detection of buried pipes from time-of-flight radar data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2254–2266. doi: 10.1109/tgrs.2008.917211

WINDSOR C G, CAPINERI L, and FALORNI P. A data pair-labeled generalized Hough transform for radar location of buried objects[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 124–127. doi: 10.1109/LGRS.2013.2248119

BOOKSTEIN F L. Fitting conic sections to scattered data[J]. Computer Graphics and Image Processing, 1979, 9(1): 56–71. doi: 10.1016/0146-664x(79)90082-0

AKIMA H. A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points[J]. ACM Transactions on Mathematical Software, 1978, 4(2): 148–159. doi: 10.1145/355780.355786

PORRILL J. Fitting ellipses and predicting confidence envelopes using a bias corrected Kalman filter[J]. Image and Vision Computing, 1990, 8(1): 37–41. doi: 10.1016/0262-8856(90)90054-9

YOUN H S and CHEN C C. Automatic GPR target detection and clutter reduction using neural network[J]. SPIE, 2002, 4758: 579–582. doi: 10.1117/12.462229.

MAAS C and SCHMALZL J. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar[J]. Computers & Geosciences, 2013, 58: 116–125. doi: 10.1016/j.cageo.2013.04.012

VIOLA P and JONES M J. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137–154. doi: 10.1023/b:visi.0000013087.49260.fb

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386

WITTEN T R. Present state of the art in ground-penetrating radars for mine detection[J]. SPIE, 1998, 3392.

PHAM M T and LEFÈVRE S. Buried object detection from B-scan ground penetrating radar data using Faster-RCNN[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 6804–6807.

LEI Wentai, HOU Feifei, XI Jingchun, et al. Automatic hyperbola detection and fitting in GPR B-scan image[J]. Automation in Construction, 2019, 106: 102839. doi: 10.1016/j.autcon.2019.102839

DOU Qingxu, WEI Lijun, MAGEE D R, et al. Real-time hyperbola recognition and fitting in GPR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 51–62. doi: 10.1109/tgrs.2016.2592679

ZHOU Xiren, CHEN Huanhuan, and LI Jinlong. An automatic GPR B-Scan image interpreting model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3398–3412. doi: 10.1109/TGRS.2018.2799586

TANOLI W A, SHARAFAT A, and PARK J, et al. Damage Prevention for underground utilities using machine guidance[J]. Automation in Construction, 2017, 107: 102893.

YALÇINER C C, BANO M, KADIOGLU M, et al. New temple discovery at the archaeological site of Nysa (western Turkey) using GPR method[J]. Journal of Archaeological Science, 2009, 36(8): 1680–1689. doi: 10.1016/j.jas.2008.12.016

CAPINERI L, GRANDE P, and TEMPLE J A G. Advanced image-processing technique for real-time interpretation of ground-penetrating radar images[J]. International Journal of Imaging Systems and Technology, 1998, 9(1): 51–59. doi: 10.1002/(SICI)1098-1098(1998)9:1<51::AID-IMA7>3.0.CO;2-Q

PASOLLI E, MELGANI F, DONELLI M, et al. Automatic detection and classification of buried objects in GPR images using genetic algorithms and support vector machines[C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, 2008: Ⅱ-525–Ⅱ-528.

PASOLLI E, MELGANI F, and DONELLI M. Automatic analysis of GPR images: A pattern-recognition approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(7): 2206–2217. doi: 10.1109/TGRS.2009.2012701

MOLYNEAUX T C K, MILLARD S G, BUNGEY J H, et al. Radar assessment of structural concrete using neural networks[J]. NDT & E International, 1995, 28(5): 281–288.

AL-NUAIMY W, HUANG Y, NAKHKASH M, et al. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition[J]. Journal of Applied Geophysics, 2000, 43(2/4): 157–165.

GAMBA P and LOSSANI S. Neural detection of pipe signatures in ground penetrating radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(2): 790–797. doi: 10.1109/36.842008

AL-NUAIMY W, HUANG Y, NAKHKASH M, et al. Neural network for the automatic detection of buried utilities and landmines[C]. 1998 Progress of Electromagnetic Research Symposium, Nantes, Frances, 1998: 141.

SHIHAB S, AL-NUAIMY W, HUANG Y, et al. Automatic region-based shape discrimination of ground penetrating radar signatures[C]. 2003 Symposium on the Application of Geophysics to Environmental and Engineering Problems SAGEEP 2003, San Antonio, USA, 2003.

AL-NUAIMY W, LU Huihai, SHIHAB S, et al. Automatic mapping of linear structures in 3-dimensional space from ground-penetrating radar data[C]. 2001 IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Rome, Italy, 2001: 198–201.

SHIHAB S, AL-NUAIMY W, and ERIKSEN A. Image processing and neural network techniques for automatic detection and interpretation of ground penetrating radar data[C]. The 6th WSEAS, Crete, 2002.

AL-NUAIMY W, HUANG Yi, ERIKSEN A, et al. Automatic detection of hyperbolic signatures in ground-penetrating radar data[J]. SPIE, 2001, 4491: 327.

SHAW M R, MOLYNEAUX T C K, MILLARD S G, et al. Assessing bar size of steel reinforcement in concrete using ground penetrating radar and neural networks[J]. Insight - Non-Destructive Testing and Condition Monitoring, 2003, 45(12): 813–816. doi: 10.1784/insi.45.12.813.52980

SHAW M R, MILLARD S G, MOLYNEAUX T C K, et al. Location of steel reinforcement in concrete using ground penetrating radar and neural networks[J]. NDT & E International, 2005, 38(3): 203–212.

LAMERI S, LOMBARDI F, BESTAGINI P, et al. Landmine detection from GPR data using convolutional neural networks[C]. The 25th European Signal Processing Conference, Kos, Greece, 2017.

BENGIO Y. Learning deep architectures for AI[J]. Foundations and Trends® in Machine Learning, 2009, 2(1): 1–127. doi: 10.1561/2200000006

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

KRIZHEVSKY A. Learning multiple layers of features from tiny images[R]. Technical Report TR-2009, 2009: 1–60.

KANUNGO T, MOUNT D M, NETANYAHU N S, et al. An efficient k-means clustering algorithm: Analysis and implementation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881–892. doi: 10.1109/TPAMI.2002.1017616

NG R T and HAN Jiawei. Efficient and effective clustering methods for spatial data mining[C]. The 20th International Conference on Very Large Data Bases, Birmingham, USA, 1994: 144–155.

ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]. The 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 1996: 226–231.

ERTÖZ L, STEINBACH M, and KUMAR V. Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data[C]. The 2nd SIAM International Conference on Data Mining, 2003.

AHN S J, RAUH W, and WARNECKE H J. Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola[J]. Pattern Recognition, 2001, 34(12): 2283–2303. doi: 10.1016/S0031-3203(00)00152-7

FITZGIBBON A, PILU M, and FISHER R B. Direct least square fitting of ellipses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 476–480. doi: 10.1109/34.765658

GANDER W, GOLUB G H, and STREBEL R. Least-squares fitting of circles and ellipses[J]. BIT Numerical Mathematics, 1994, 34(4): 558–578. doi: 10.1007/BF01934268

PILU M, FITZGIBBON A W, and FISHER R B. Ellipse-specific direct least-square fitting[C]. The 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996: 599–602.

CHEN Huanhuan and COHN A G. Probabilistic conic mixture model and its applications to mining spatial ground penetrating radar data[C]. The Workshops in SIAM Conference on Data Mining, 2010: 1–9.

CHEN Huanhuan and COHN A G. Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data[C]. 2010 International Joint Conference on Neural Networks, Barcelona, Spain, 2010: 1–8.

Relative Articles

Supplements(0)

Cited By

Proportional views