GPR radargrams analysis through machine learning approach

References

• Daniels DJ. Ground penetrating radar. 2nd ed. London (UK): IET; 2004.
   Google Scholar
• Batool S, Frezza F, Mangini F, et al. Introduction of radar scattering application in remote sensing and diagnostics: review. Atmosphere. 2020;11:517.
   Web of Science ®Google Scholar
• Mechbal Z, Khamlichi A, Determination of concrete rebars characteristics by enhanced post-processing of GPR scan raw data. NDT E Int. Jul. 2017;89:30–39.
   Web of Science ®Google Scholar
• Di Gregorio PP, Frezza F, Mangini F, et al. Detection of a misaligned broken pipe by electromagnetic interaction. EGU General Assembly 2016; 2016 Apr 17–22; Vienna Austria. id. EPSC2016-18501; 2016.
   Google Scholar
• Di Gregorio PP, Frezza F, Mangini F, et al. Determination of concrete cover thickness in a reinforced concrete pillar by observation of the scattered electromagnetic field. 19th EGU General Assembly, EGU2017, Proceedings from the Conference; 2017 Apr 23–28; Vienna, Austria; 2017. p. 13784.
   Google Scholar
• Mangini F, Di Gregorio PP, Muzi M, et al. Wire-Grid modeling of metallic targets for ground penetrating radar applications. Proc IMEKO Int Conf Metrology Archaeol.
   Google Scholar
• Ferrara V, Troiani F, Frezza F, et al. Design and realization of a cheap ground penetrating radar prototype @ 2.45 GHz. 2016 10th European Conference on Antennas and Propagation, EuCAP, 7482008; 2016.
   Google Scholar
• Zhan R, Xie H, GPR measurement of the diameter of steel bars in concrete specimens based on the stationary wavelet transform. Insight Non-Destructive Test Condition Monit. 2009;51(3):151–155.
   Google Scholar
• Liu T, Klotzsche A, Pondkule M, et al. Estimation of subsurface cylindrical object properties from GPR full-waveform inversion. In: Proc 9th Int Workshop Adv Ground Penetrating Radar (IWAGPR); Jun 2017. p. 1–4.
   Google Scholar
• Drobiec Ł, Jasinski R, Mazur W, et al. Accuracy of eddy-current and radar methods used in reinforcement detection. Materials. 2019;12(7):1168.
   Web of Science ®Google Scholar
• Zhou F, Chen Z, Liu H, et al. Simultaneous estimation of rebar diameter and cover thickness by a GPR-EMI dual sensor. Sensors. 2018;18(9):2969.
   PubMed Web of Science ®Google Scholar
• Al-Nuaimy , Huang W, Nakhkash Y, et al. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J Appl Geophys. 2000;43(2-4):157–165.
   Web of Science ®Google Scholar
• Caorsi S, Cevini G. An electromagnetic approach based on neural networks for the GPR investigation of buried cylinders. IEEE Geosci Remote Sens Lett. 2005;2(1):3–7.
   Web of Science ®Google Scholar
• Travassos XL, Vieira DAG, Ida N, et al. Characterization of inclusions in a nonhomogeneous GPR problem by artificial neural networks. IEEE Trans Magnetics. 2008;44(6):1630–1633.
   Web of Science ®Google Scholar
• Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, MA: MIT Press; 2016.
   Google Scholar
• Goodfellow MT, Lefévre S. Deep learning buried object detection from B-scan ground penetrating radar data using faster-RCNN. In: IGARSS 2018 – 2018 IEEE Int Geosci Remote Sensing Symp; 2018 Jul; Valencia, Spain. p. 6804–6807.
   Google Scholar
• Kafedziski V, Pecov S, Tanevski D. Detection and classification of land mines from ground penetrating radar data using faster R-CNN. In: 2018 26th Telecommunications Forum (TELFOR); 2018 Nov; Belgrade, Serbia. IEEE. p. 1–4.
   Google Scholar
• Girshick R. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision; 2015 Dec; Santiago, Chile. p. 1440–1448.
   Google Scholar
• Giannakis I, Giannopoulos A, Warren C. A machine learning-based fast-forward solver for ground penetrating radar with application to full-Waveform inversion. IEEE Trans Geosci Remote Sensing. 2019;57(7):4417–4426. DOI:10.1109/TGRS.2019.2891206
   Web of Science ®Google Scholar
• Giannakis I, Giannopoulos A, Warren C. A machine learning scheme for estimating the diameter of reinforcing bars using ground penetrating radar. IEEE Geosci Remote Sensing Lett. DOI:10.1109/LGRS.2020.2977505
   Web of Science ®Google Scholar
• Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks. In: Proc IEEE Conf Comput Vision Pattern Recognition. 2017 Jul; Honolulu, USA. p. 4700–470.
   Google Scholar
• Ponti F, Barbuto F, Di Gregorio PP, et al. Deep learning for applications to ground penetrating radar and electromagnetic diagnostic. PIERS; 2019.
   Google Scholar
• Warren C, Giannopoulos A, Giannakis I. gprMax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput Phys Comm. 2016;209:163–170.
   Web of Science ®Google Scholar
• Taflove A, Hagness SC. Computational electrodynamics: the finite-difference time-domain method. 2nd ed. Norwood (MA): Artech House; 2000.
   Google Scholar
• Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980; 2014.
   Google Scholar