面向探地雷达 B-scan图像的目标检测算法综述

侯斐斐; 施荣华; 雷文太; 董健; 许孟迪; 席景春

doi:10.11999/JEIT190680

面向探地雷达 B-scan图像的目标检测算法综述

doi: 10.11999/JEIT190680

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
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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

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，并回顾了国内外学者在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。
- 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.
- Ground Penetrating Radar (GPR) /
- Underground target detection /
- Machine Learning (ML) /
- Deep Learning (DL) /
- Hyperbolic reflection

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图 1 真实场景中GPR B-scan中的双曲线特征

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图 2 在文献[28]中解决的一些复杂情况示例

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图 3 DCSE和OSCA算法的对比结果展示

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图 4 基于CTFP算法的双曲线拟合结果展示

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表 1 GPR目标检测的经典算法总结

序号	参考文献	时间	GPR目标	客观评价
1	Borgioli et al.^[17]	2008	地埋管道	在Hough变换中引入加权因子，解决了管道靠近时双曲线重叠的问题；但是需要预备模型，计算成本相对较高。
2	Maas et al. ^[23]	2013	双曲线反射	使用Viola-Jones算法标记目标候选区域，它避免了模板匹配并缩小了后续搜索区域；然而，应用特征需手动识别，分类结果取决于特征的质量，难度随着数据量的增加。
3	Besaw et al.^[2]	2016	地埋爆炸物	应用CNN从GPR B-scan中提取有意义的特征并对目标进行分类。交叉验证，网络权重正则化和“dropout”用于防止过度训练。
4	Besaw^[3]	2016	地埋爆炸物	在CNN基础上增加了额外的Data Augmentation技术，用于增加可用训练数据的数量和可变性。
5	文献[4,5]	2017	地埋爆炸物	研究了预训练CNN的初始化步骤，以解决GPR数据标记样本不足的问题；但是输入网络中真实图像的大小和数量通常是有限的，仅实现分类步骤。
6	Pham et al. ^[27]	2018	双曲线反射	首次采用Faster RCNN来检测GPR B-scan中的反射双曲线。该技术在真实测试集上的性能要超过使用HOG或Haar-like特征的检测器，但缺少定量的评估。
7	Lei et al. ^[28]	2019	地埋钢筋	在文献[27]基础上，采用了DA手段增加真实GPR数据集和仿真数据集；提出DCSE算法以识别双曲线特征，完善了文献[30]中提出的OSCA算法；提出CTFP算法自动提取拟合点。所提出方案的有效性在仿真和真实数据集上得到了验证。
8	Dou et al. ^[29]	2016	双曲线反射	提出了C3算法分割交叉双曲线，并将其送入神经网络进行分类。C3算法水平扫描B-scan图像中的每个像素以进行聚类。然而，双曲线是垂直向下打开的，C3算法没有考虑这个重要特征。
9	Zhou et al. ^[30]	2018	金属管道水泥管道	提出OSCA算法解决了文献[29]中的难题，可以识别具有向下开口特征的聚类。然而，在整个图像上进行OSCA算法是不合适的，因为难以处理包含太多非平稳噪声的大型现场数据集，导致后续处理复杂化。

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