TY - JOUR T1 - Buried Objects Segmentation and Detection in GPR B Scan Images AU - Altın, Gozde AU - Dolma, Arif PY - 2019 DA - July JF - The Eurasia Proceedings of Science Technology Engineering and Mathematics JO - EPSTEM PB - ISRES Publishing WT - DergiPark SN - 2602-3199 SP - 11 EP - 17 VL - 6 LA - en AB - Identificationof buried objects through Ground Penetrating Radar B scan (GPR-B) images needshigh computational techniques and long processing time due to curve fitting orpattern recognition methods. In this study, an efficient and fast recognitionsystem is proposed for detection of buried objects region. Previously, GPR-Bscan images of objects with different shapes in various depths were obtained byusing gprMax simulation program. The detection process is categorized into foursteps. The GPR-B images are transformed at first step. Then, they arethresholded to obtain potential buried object regions. Third step of the systemis hough transform in order to eliminate ground surface. Finally, an estimatedregion analysis is performed. The results show high performance with fullyautomatic segmentation. The processing time for detection of buried object isin the range of 1.234 - 2.232 seconds. It can be observed that this technique isfaster than other studies in the literature. Consequently, it may be used inreal time applications for GPR devices. KW - Ground penetrating radar KW - GprMax KW - image processing KW - Hough transform CR - 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. UR - https://dergipark.org.tr/en/pub/epstem/article/596925 L1 - https://dergipark.org.tr/en/download/article-file/770654 ER -