Underground target detection and location estimation based on scattering curve feature recognition network

Shuo Zhang; Qiang Ren; Wentai Lei; Qian Song; Jiabin Luo; Shiguang Luo; Yiwei Wang; Long Xu

doi:10.1117/12.2617695

15 February 2022 Underground target detection and location estimation based on scattering curve feature recognition network

Shuo Zhang, Qiang Ren, Wentai Lei, Qian Song, Jiabin Luo, Shiguang Luo, Yiwei Wang, Long Xu

Author Affiliations +

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121665V (2022) https://doi.org/10.1117/12.2617695
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Ground penetrating radar (GPR) is a non-destructive detection method, which is widely used in shallow underground target detection. The development of machine learning and artificial intelligence technology has promoted the development of GPR data processing at the aspect of automatic and intelligent. This paper proposes a neural network for recognizing the characteristics of underground targets scattering curve from GPR B-scan data. Firstly, GPR B-scan echo data is preprocessed in three steps: data normalization, data standardization and sample division to product samples. Secondly, a new neural network is designed to identify the characteristic points of scattering curve in three adjacent A-scan echo data. According to characteristic point categories, underground targets location and depth can be obtained by adopting the corresponding relationship between time delay and depth. Compared with the existing methods, this method has the advantages of automation, rapid processing and no need for expert decision-making. Simulation and on-site data processing experiments are carried out and its ability on accuracy and higher position estimation accuracy for incomplete data is verified.

Citation Download Citation

Shuo Zhang, Qiang Ren, Wentai Lei, Qian Song, Jiabin Luo, Shiguang Luo, Yiwei Wang, and Long Xu "Underground target detection and location estimation based on scattering curve feature recognition network", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121665V (15 February 2022); https://doi.org/10.1117/12.2617695

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KEYWORDS

Target detection

General packet radio service

Scattering

Data processing

Neural networks

Data modeling

Image processing

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