DABP：一种基于深度学习的探地雷达自聚焦后向投影成像方法

雷文太; 隋浩; 姜和俊; 吕荣其

doi:10.12263/DZXB.20231144

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DABP：一种基于深度学习的探地雷达自聚焦后向投影成像方法

学术论文 | 更新时间：2025-12-11

- DABP：一种基于深度学习的探地雷达自聚焦后向投影成像方法
- DABP: A Deep Learning Based Auto Focusing Back Projection Imaging Method for Ground Penetrating Radar
- 电子学报 2024年52卷第12期页码：4023-4036
- 作者机构：
  
  1.中南大学电子信息学院，湖南长沙 410083
  2.近地面探测技术重点实验室，江苏无锡 214035
- 作者简介：
  
  [ "隋浩男，1999年9月生，湖南长沙人.中南大学电子信息学院硕士研究生.主要研究方向为探地雷达信号处理技术. E-mail: 214711018@csu.edu.cn" ]
  [ "姜和俊男，1968年5月生，江苏海安人.近地面探测技术重点实验室高级工程师.主要从事爆炸物探测技术研究. E-mail: jhj68@126.com" ]
  [ "吕荣其男，1996年4月生，安徽宿州人.中国矿业大学硕士研究生.主要研究方向为电磁探测、信息技术. E-mail: 2395404218@qq.com" ]
- 基金信息：
  
  近地面探测技术重点实验室基金(6142414221605)
- DOI：10.12263/DZXB.20231144
  中图分类号： TN957.52;
- 收稿：2023-12-08，
  
  修回：2024-06-26，
  
  纸质出版：2024-12-25
- 稿件说明：
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雷文太, 隋浩, 姜和俊, 等. DABP：一种基于深度学习的探地雷达自聚焦后向投影成像方法[J]. 电子学报, 2024, 52(12): 4023-4036.

LEI Wen-tai, SUI Hao, JIANG He-jun, et al. DABP: A Deep Learning Based Auto Focusing Back Projection Imaging Method for Ground Penetrating Radar[J]. Acta Electronica Sinica, 2024, 52(12): 4023-4036.
雷文太, 隋浩, 姜和俊, 等. DABP：一种基于深度学习的探地雷达自聚焦后向投影成像方法[J]. 电子学报, 2024, 52(12): 4023-4036. DOI：10.12263/DZXB.20231144

LEI Wen-tai, SUI Hao, JIANG He-jun, et al. DABP: A Deep Learning Based Auto Focusing Back Projection Imaging Method for Ground Penetrating Radar[J]. Acta Electronica Sinica, 2024, 52(12): 4023-4036. DOI：10.12263/DZXB.20231144

摘要

探地雷达（Ground Penetrating Radar， GPR）作为一种非破坏性的电磁探测技术，已广泛应用于市政工程、交通、军事等领域的探测.复杂的地下环境中，电磁波传播规律变得复杂，背景介质的介电常数难以准确获得.后向投影（Back Projection，BP）成像算法需要预知背景介质的相对介电常数，且需逐个计算各成像网格的散射强度值，计算效率低.本文提出一种基于深度学习的探地雷达自聚焦后向投影（Deep learning based Auto focusing BP，DABP）成像方法，设计了目标感兴趣区域（Region Of Interest，ROI）的检测模块，基于地下目标的空间稀疏特征，将YOLOX网络和BP成像机理相结合，快速检测出目标潜在区域，仅对该区域中的成像网格进行成像处理，避免全域的后向投影计算，大幅降低运算量.其次，针对介电常数未知情况下BP成像难以聚焦的问题，设计了一个自聚焦后向投影（Auto Focusing BP，AF-BP）成像模块，构建了BS-YOLOv5网络和相应的数据集，实现基于改进二分法的地下介质介电常数估计和自聚焦成像.然后，设计了一个基于双阈值和积分聚焦的伪影抑制（artifact suppression based on Double Threshold and Integral Focusing，DTIF）模块，进一步提高成像结果的聚焦度.开展了仿真和实测数据的成像处理和对比分析，与BP成像方法相比，仿真数据成像结果的ISLR指标下降了250%、SCR指标提升了131%；实测数据成像结果的ISLR指标下降了322%、SCR指标提升了72%，仿真实验和实测实验的成像速度均提升了300%，验证了所提方法在提高GPR成像效率和成像质量方面的有效性.

Abstract

As a non-destructive electromagnetic detection technology

ground penetrating radar (GPR) has been widely used in municipal engineering

transportation

military and other fields. In the complex underground environment

the propagation law of electromagnetic wave becomes complicated

and the dielectric constant of background medium is difficult to be accurately obtained. Back projection (BP) imaging algorithm needs to predict the relative dielectric constant of the background media and calculate the scattering intensity of each imaging grid one by one

so the calculation efficiency is low. This paper puts forward the imaging method of deep learning based auto focusing BP (DABP). Firstly

a region of interest (ROI) detection module is designed. Based on the sparse space characteristics of underground targets

by combining YOLOX network and BP imaging mechanism

the potential target region is quickly detected

and only the imaging grid in the region is processed

which avoids the global back projection calculation and greatly reduces the amount of computation. Secondly

aiming at the problem that BP imaging is difficult to focus when the dielectric constant is unknown

an auto focusing BP (AF-BP) imaging module is designed

BS-YOLOv5 network and corresponding data set are constructed

and dielectric constant estimation and auto focusing imaging of underground media based on improved dichotomy are realized. Then

an artifact suppression based on double threshold and integral focusing (DTIF) module is designed to further improve the focusing degree of imaging results. Compared with BP imaging method

ISLR index of simulation data decreased by 250% and SCR index increased by 131%. ISLR index of measured data imaging results decreased by 322%

SCR index increased by 72%

and imaging speed of simulation experiment and measured experiment increased by 300%

which verified the effectiveness of the proposed method in improving GPR imaging efficiency and imaging quality.

关键词

Keywords

references

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