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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

引用本文: 侯斐斐, 施荣华, 雷文太, 董健, 许孟迪, 席景春. 面向探地雷达 B-scan图像的目标检测算法综述[J]. 电子与信息学报, 2020, 42(1): 191-200. doi: 10.11999/JEIT190680 shu
Citation:  Feifei HOU, Ronghua SHI, Wentai LEI, Jian DONG, Mengdi XU, Jingchun XI. A Review of Target Detection Algorithm for GPR B-scan Processing[J]. Journal of Electronics and Information Technology, 2020, 42(1): 191-200. doi: 10.11999/JEIT190680 shu

面向探地雷达 B-scan图像的目标检测算法综述

    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
  • 基金项目: 国家自然科学基金(61102139和61872390),中南大学基础研究基金(2018zzts181)

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。

English

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  • 图 1  真实场景中GPR B-scan中的双曲线特征

    图 2  在文献[28]中解决的一些复杂情况示例

    图 3  DCSE和OSCA算法的对比结果展示

    图 4  基于CTFP算法的双曲线拟合结果展示

    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
    1Borgioli 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。
    2Maas 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。
    3Besaw 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。
    4Besaw[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。
    6Pham 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。
    7Lei 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。
    8Dou 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。
    9Zhou 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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文章相关
  • 通讯作者:  雷文太, leiwentai@csu.edu.cn
  • 收稿日期:  2019-09-04
  • 录用日期:  2019-11-12
  • 网络出版日期:  2019-11-18
  • 刊出日期:  2020-01-01
通讯作者: 陈斌, bchen63@163.com
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