Browse > Article
http://dx.doi.org/10.9711/KTAJ.2022.24.4.341

Comparison of performance of automatic detection model of GPR signal considering the heterogeneous ground  

Lee, Sang Yun (Dept. of Civil Engineering, Inha University)
Song, Ki-Il (Dept. of Civil Engineering, Inha University)
Kang, Kyung Nam (Research Institute of Construction & Environmental System, Inha University)
Ryu, Hee Hwan (Structural & Seismic Technology Group, Korea Electric Power Corporation Research Institute)
Publication Information
Journal of Korean Tunnelling and Underground Space Association / v.24, no.4, 2022 , pp. 341-353 More about this Journal
Abstract
Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.
Keywords
Ground penetrating radar; Finite difference time domain (FDTD); Fractal model; Convolutional neural network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Warren, C., Giannopoulos, A. gprMax user guide, https://docs.gprmax.com/en/latest/ (Apr 14, 2022)
2 Warren, C., Giannopoulos, A., Giannakis, I. (2016), "gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar", Computer Physics Communications, Vol. 209, pp. 163-170.   DOI
3 Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016), "You only look once: Unified, real-time object detection", Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788.
4 Taflove, A., Hagness, S.C., Piket-May, M. (2005), Computational Electrodynamics: The Finite-Difference Time-Domain Method, The Electrical Engineering Handbook, 3, Elsevier, Burlington, pp. 629-670.
5 Giannopoulos, A. (1998), The investigation of transmission-line matrix and finite-difference time-domain methods for the forward problem of ground probing radar, Ph.D. Thesis, University of York, pp. 1-258.
6 Al-Nuaimy, W., Huang, Y., Nakhkash, M., Fang, M.T.C., Nguyen, V.T., Eriksen, A. (2000), "Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition", Journal of applied Geophysics, Vol. 43, No. 2-4, pp. 157-165.   DOI
7 Benedetto, A., Pajewski, L. (2015), Civil Engineering Applications of Ground Penetrating Radar, Springer, London, pp. i-xi.
8 Giannopoulos, A. (2005), "Modelling ground penetrating radar by GprMax", Construction and Building Materials, Vol. 19, No. 10, pp. 755-762.   DOI
9 Chae, J.H., Ko, H.Y., Lee, B.G., Kim, N.G. (2019), "A study on the pipe position estimation in GPR images using deep learning based convolutional neural network", Journal of Internet Computing and Services, Vol. 20, No. 4, pp. 39-46.
10 Fang, Y., Guo, X., Chen, K., Zhou, Z., Ye, Q. (2021), "Accurate and automated detection of surface knots on sawn timbers using YOLO-V5 model", BioResources, Vol. 16, No. 3, pp. 5390-5406.   DOI
11 Lee, D.Y. (2018), "Analysis of sewer pipe defect and ground subsidence risk by using CCTV and GPR monitering results", Journal of the Korean Geosynthetics Society, Vol. 17, No. 3, pp. 47-55.   DOI
12 Peplinski, N.R., Ulaby, F.T., Dobson, M.C. (1995), "Dielectric properties of soils in the 0.3-1.3-GHz range", IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 3, pp. 803-807.   DOI
13 Pham, M.T., Lefevre, S. (2018), "Buried object detection from B-scan ground penetrating radar data using Faster-RCNN", Proceedings of the IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp. 6804-6807.
14 Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M. (2020), "Yolov4: Optimal speed and accuracy of object detection", arXiv preprint arXiv:2004.10934, pp. 1-17.
15 Giannakis, I. (2016), Realistic numerical modelling of ground penetrating radar for landmine detection, Ph.D. Thesis, University of Edinburgh, pp. 1-268.
16 Kim, H.M., Bae, H.R. (2021), "A study on GPR image classification by semi-supervised learning with CNN", The Journal of Bigdata, Vol. 6, No. 1, pp. 197-206.
17 Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., Liang, Z. (2019), "Apple detection during different growth stages in orchards using the improved YOLO-V3 model", Computers and Electronics in Agriculture, Vol. 157, pp. 417-426.   DOI
18 Yuan, C., Li, S., Cai, H., Kamat, V.R. (2018), "GPR signature detection and decomposition for mapping buried utilities with complex spatial configuration", Journal of Computing in Civil Engineering, Vol. 32, No. 4, pp. 1-15.