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基于深度学习的探地雷达二维剖面图像结构特征检测方法

王辉 欧阳缮 刘庆华 廖可非 周丽军

王辉, 欧阳缮, 刘庆华, 廖可非, 周丽军. 基于深度学习的探地雷达二维剖面图像结构特征检测方法[J]. 电子与信息学报, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032
引用本文: 王辉, 欧阳缮, 刘庆华, 廖可非, 周丽军. 基于深度学习的探地雷达二维剖面图像结构特征检测方法[J]. 电子与信息学报, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032
WANG Hui, OUYANG Shan, LIU Qinghua, LIAO Kefei, ZHOU Lijun. Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032
Citation: WANG Hui, OUYANG Shan, LIU Qinghua, LIAO Kefei, ZHOU Lijun. Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1284-1294. doi: 10.11999/JEIT211032

基于深度学习的探地雷达二维剖面图像结构特征检测方法

doi: 10.11999/JEIT211032
基金项目: 国家自然科学基金(61871425, 61861011, 61631019),广西创新驱动发展专项(桂科AA21077008),山西省交通运输厅科技项目(2019-1-18)
详细信息
    作者简介:

    王辉:男,1982年生,博士生,副教授,研究方向为探地雷达信号处理、深度学习

    欧阳缮:男,1960年生,博士,教授,博士生导师,研究方向为智能信息处理

    刘庆华:女,1973年生,博士,教授,硕士生导师,研究方向为智能信息处理、深度学习

    廖可非:男,1984年生,博士,副教授,硕士生导师,研究方向为探地雷达信号处理

    周丽军:女,1984年生,博士,高级工程师,研究方向为探地雷达信号处理

    通讯作者:

    欧阳缮 hmoysh@guet.edu.cn

  • 中图分类号: TN957.52

Structure Feature Detection Method for Ground Penetrating Radar Two-Dimensional Profile Image Based on Deep Learning

Funds: The National Natural Science Foundation of China (61871425, 61861011, 61631019), Guangxi Special Fund Project for Innovation-driven Development (GuikeAA21077008), Shanxi Transportation Department Projects (2019-1-18)
  • 摘要: 该文针对探地雷达(GPR) 2维剖面图像中目标特征提取困难及其识别精度较低等问题,采用深度学习方法来提取2维剖面图像中目标的特征双曲线。根据GPR工作的物理机制,设计了一种级联结构的卷积神经网络(CNN),先检测并去除回波数据中的直达波干扰信号,再利用CNN得到B扫描(B-SCAN)图像的特征图,并对特征信号进行分类识别以提取目标的特征双曲线。同时,为处理各种干扰信号影响目标特征双曲线结构完整性的问题,提出了一种基于方向引导的特征数据补全方法,提高了目标特征双曲线识别的准确率。与方向梯度直方图(HOG)算法、单级式目标检测(YOLOV3)算法和更快速的区域卷积神经网络(Faster RCNN)算法相比,在综合评价指标F上该文方法的检测结果是最优的。
  • 图  1  目标特征检测框架

    图  2  GPR反射测量模式及仿真2维剖面图像

    图  3  B-SCAN图像特征提取的级联CNN结构

    图  4  目标特征双曲线提取网络训练模型的损失曲线

    图  5  结构信息不完整的GPR B-SCAN特征双曲线示意图

    图  6  提取GPR B-SCAN图像的特征图

    图  7  目标双曲线特征数据补全

    图  8  B-SCAN图像中的目标特征双曲线检测结果对比

    图  9  不同算法的F均值统计结果

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出版历程
  • 收稿日期:  2021-09-27
  • 修回日期:  2022-02-18
  • 录用日期:  2022-02-23
  • 网络出版日期:  2022-03-07
  • 刊出日期:  2022-04-18

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