施荣华,
雷文太,,
董健,
许孟迪,
席景春
中南大学计算机学院 长沙 410000
基金项目:国家自然科学基金(61102139, 61872390)，中南大学基础研究基金(2018zzts181)
详细信息
作者简介:侯斐斐：女，1993年生，博士生，研究方向为探地雷达，深度学习，图像处理
施荣华：男，1963年生，教授，博士生导师，研究方向为射频系统集成和量子技术
雷文太：男，1979年生，副教授，博士生导师，研究方向为探地雷达系统集成和信号处理
通讯作者:雷文太 leiwentai@csu.edu.cn
中图分类号:TN957.51计量
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被引次数:0
出版历程
收稿日期:2019-09-04
修回日期:2019-11-12
网络出版日期:2019-11-18
刊出日期:2020-01-21
A Review of Target Detection Algorithm for GPR B-scan Processing
Feifei HOU,Ronghua SHI,
Wentai LEI,,
Jian DONG,
Mengdi XU,
Jingchun XI
Department of Computer Science and Engineering, Central South University, Changsha 410000, China
Funds:The National Natural Science Foundation of China (61102139, 61872390), The Fundamental Rresearch Funds for the Central Universities of Central South University (2018zzts181)
摘要
摘要:利用无损探测技术来获取地下目标的信息是当前研究的热点，探地雷达(GPR)作为一种重要的无损工具，已被广泛用于检测，定位和特征化地下目标。然而，从GPR成像中探测掩埋物体并评估其位置既费时又费力。因此，实现地下目标的自动化探测对实际应用是必要的。为此，该文在综合分析地下目标回波特征的基础上，讨论了使用GPR评估目标位置的可行性，并回顾了国内外****在GPR成像中对双曲线特征自动化检测的研究进展。该文还在国内外典型实例剖析的基础上，总结并比较了目标检测的处理方法。最后指出，未来的研究应集中于开发新的深度学习检测框架，用以自动检测和估计真实场景中的地下特征。
关键词:探地雷达/
地下目标检测/
机器学习/
深度学习/
双曲线反射
Abstract:Ground Penetrating Radar (GPR), as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discusses the feasibility of using GPR to estimate target positions, and reviews the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.
Key words:Ground Penetrating Radar (GPR)/
Underground target detection/
Machine Learning (ML)/
Deep Learning (DL)/
Hyperbolic reflection
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