Buried Objects Segmentation and Detection in GPR B Scan Images

Abstract

Identification of buried objects through Ground Penetrating Radar B scan (GPR-B) images needs high computational techniques and long processing time due to curve fitting or pattern recognition methods. In this study, an efficient and fast recognition system is proposed for detection of buried objects region. Previously, GPR-B scan images of objects with different shapes in various depths were obtained by using gprMax simulation program. The detection process is categorized into four steps. The GPR-B images are transformed at first step. Then, they are thresholded to obtain potential buried object regions. Third step of the system is hough transform in order to eliminate ground surface. Finally, an estimated region analysis is performed. The results show high performance with fully automatic segmentation. The processing time for detection of buried object is in the range of 1.234 - 2.232 seconds. It can be observed that this technique is faster than other studies in the literature. Consequently, it may be used in real time applications for GPR devices.

Keywords

• Ground penetrating radar,GprMax,image processing,Hough transform

References

1. Levent Seyfi, Ercan Yaldız, A simulator based on energy efficient GPR algorithm modified for the scanning of all types of regions, Turk J Elec Eng & Comp Sci, Vol. 20 (3), 381-389, 2012. DOI: 10.3906/elk-1011-955 L. Capineri, P. Grande, and J. A. G. Temple, Advanced image-processing technique for real-time interpretation of ground-penetrating radar images, Int. J. Imaging Syst. Technol., vol. 9, no. 1, pp. 51–59, 1998. H. Brunzell, “Detection of shallowly buried objects using impulse radar,” IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 875–886, Mar. 1999. S. Delbò, P. Gamba, and D. Roccato, “A fuzzy Shell clustering approach to recognize hyperbolic signatures in subsurface radar images,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1447–1451, May 2000. P. Gamba and S. Lossani, “Neural detection of pipe signatures in ground penetrating radar images,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 790–797, Mar. 2000. W. Al-Nuaimy, Y. Huang, M. Nakhkash,M. T. C. Fang, V. T. Nguyen, and A. Eriksen, “Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition,” J. Appl. Geophys., vol. 43, no. 2– 4, pp. 157–165, Mar. 2000. H. S. Youn and C. C. Chen, “Automatic GPR target detection and clutter reduction using neural network,” in Proc. 9th Int. Conf. Ground Penetrating Radar, Santa Barbara, CA, 2002, vol. 4758, pp. 579–582. M. Rossini, “Detecting objects hidden in the subsoil by a mathematical method,” Comput. Math. Appl., vol. 45, no. 1, pp. 299–307, Jan. 2003. S. Shihab, W. Al-Nuaimy, and Y. Huang, “A comparison of segmentation techniques for target extraction in ground penetrating radar data,” in Proc. 2nd Int. Workshop Advanced GPR, Delft, The Netherlands, 2003, pp. 95–100. P. Gamba and V. Belotti, “Two fast buried pipe detection schemes in ground penetrating radar images,” Int. J. Remote Sens., vol. 24, no. 12, pp. 2467–2484, Jan. 2003. P. Falorni, L. Capineri, L. Masotti, and G. Pinelli, “3-D radar imaging of buried utilities by features estimation of hyperbolic diffraction patterns in radar scans,” in Proc. 10th Int. Conf. Ground Penetrating Radar, Delft, The Netherlands, 2004, vol. 1, pp. 403–406. A. Dell’Acqua, A. Sarti, S. Tubaro, and L. Zanzi, “Detection of linear objects in GPR data,” Signal Process., vol. 84, no. 4, pp. 785–799, Apr. 2004. L. Ting-Jun and Z. Zheng-Ou, “Fast extraction of hyperbolic signatures in GPR,” in Proc. ICMMT, 2007, pp. 1–3. N. P. Singh and M. J. Nene, “Buried object detection and analysis of GPR images: Using neural network and curve fitting,” 2013 Annual International Conference on Emerging Research Areas and 2013 International Conference on Microelectronics, Communications and Renewable Energy, 2013. P.Chomdee, A. Boonpoonga and A. Prayote “Fast and Efficient Detection of Buried Object for GPR Image” The 20th Asia-Pacific Conference on Communication, pp. 350- 355, 2014.