Open Access
| Issue |
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|
|
|---|---|---|
| Article Number | 03027 | |
| Number of page(s) | 6 | |
| Section | Smart Algorithms and Recognition | |
| DOI | https://doi.org/10.1051/matecconf/202030903027 | |
| Published online | 04 March 2020 | |
- Yi Su, Chunlin Huang, Wentai Lei, Theory and application of ground penetrating radar, M. Science Press, (2006). [Google Scholar]
- M.T. Jassim, A.S. Al-Banna, A.M. Al-Rahim, Mapping subsurface archaeological features using ground penetrating radar in the ancient city of Ur, Iraq, J. Archaeological Research in Asia, (2019) 149–160. [Google Scholar]
- Yanhui You, Mingbin Yang, Qihao Yu, et al. Factors influencing the reliability of grounded and floating ice distinguishing based on ground penetrating radar reflection amplitude, J. Cold Regions Science and Technology, (2018) 154: 1–8. [CrossRef] [Google Scholar]
- A. Benedetto, F, Tosti, L.B. Ciampoli, et al. An overview of ground-penetrating radar signal processing techniques for road inspections, J. Signal Processing, (2017) 201–209. [Google Scholar]
- Baojie Wu, Two-dimensional forward simulation of ground penetrating radar and its engineering examples, D. Zhejiang University, (2007). [Google Scholar]
- J. Illingworth, J. Kittler, A survey of the Hough transform, J. Computer vision, graphics, and image processing, (1988) 87–116. [Google Scholar]
- Panfeng Du, Lijian Liao, Xinan Yang, Intelligent identification of railway subgrade diseases, J. Journal of the China Railway Society, (2010) 142–146. Huasheng Zou, Research on Subgrade Disease Identification Algorithm and Application, J. Progress in geophysics, (2009) 2302–2307. [Google Scholar]
- Xinjun Xu, Research on Disease Identification Algorithm and Risk Assessment Model of Railway Subgrade Radar Detection Images, D. China University of Mining and Technology, (2018). [Google Scholar]
- Yi Sun, Xiaogang Wang, Xiaoou Tang. Deep learning face representation from predicting 10,000 classes, C. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2014) 1891–1898. [Google Scholar]
- M. Oberweger, P. Wohlhart, V. Lepetit. Hands deep in deep learning for hand pose estimation, J. arXiv, (2015). [Google Scholar]
- S. Lameri, F. Lombardi, P. Bestagini, et al, Landmine detection from GPR data using convolutional neural networks, C. 25th European Signal Processing Conference (EUSIPCO), IEEE, (2017) 508–512, [Google Scholar]
- Shaoqing Ren, Kaiming He, R. Girshick, et al. Faster r-cnn: Towards real-time object detection with region proposal networks, C. Advances in neural information processing systems, (2015) 91–99. [Google Scholar]
- C. Warren, A. Giannopiulos, I. Giannakis, gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar, J. Computer Physics Communications, (2016) 163–170. [Google Scholar]
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