近场毫米波三维成像与异物检测方法

师君 阙钰佳 周泽南 周远远 张晓玲 孙铭芳

师君, 阙钰佳, 周泽南, 等. 近场毫米波三维成像与异物检测方法[J]. 雷达学报, 2019, 8(5): 578–588. doi: 10.12000/JR18089
引用本文: 师君, 阙钰佳, 周泽南, 等. 近场毫米波三维成像与异物检测方法[J]. 雷达学报, 2019, 8(5): 578–588. doi: 10.12000/JR18089
SHI Jun, QUE Yujia, ZHOU Zenan, et al. Near-field millimeter wave 3D imaging and object detection method[J]. Journal of Radars, 2019, 8(5): 578–588. doi: 10.12000/JR18089
Citation: SHI Jun, QUE Yujia, ZHOU Zenan, et al. Near-field millimeter wave 3D imaging and object detection method[J]. Journal of Radars, 2019, 8(5): 578–588. doi: 10.12000/JR18089

近场毫米波三维成像与异物检测方法

doi: 10.12000/JR18089
基金项目: 国家自然科学基金(61671113)
详细信息
    作者简介:

    师 君(1979–),男,河南南阳人,博士,电子科技大学信息与通信工程学院副教授,主要从事SAR成像技术、雷达信号处理研究,已发表论文50余篇。E-mail: shijun@uestc.edu.cn

    阙钰佳(1993–),男,福建龙岩人,电子科技大学硕士生,主要从事阵列3维SAR成像及目标识别技术研究。E-mail: queyujia1993@163.com

    周泽南(1997–),男,海南海口人,电子科技大学硕士生,主要从事深度学习技术在SAR图像的应用研究。E-mail: 2942714332@qq.com

    周远远(1992–),男,山东金乡人,电子科技大学博士生,主要从事深度学习技术在SAR图像的应用研究。E-mail: 732156543@qq.com

    张晓玲(1964–),女,四川成都人,博士,电子科技大学信息与通信工程学院教授,主要从事SAR成像技术、雷达探测技术研究,已发表论文50余篇。E-mail: xlzhang@uestc.edu.cn

    孙铭芳(1981–),男,辽宁本溪人,硕士,北京华航无线电测量研究所高工,从事无线电导航与引信,SAR成像,雷达信号处理研究。E-mail: sunmf125@163.com

    通讯作者:

    师君 shijun@uestc.edu.cn

  • 责任主编:陈杰 Corresponding Editor: CHEN Jie
  • 中图分类号: TN957.52

Near-field Millimeter Wave 3D Imaging and Object Detection Method

Funds: The National Natural Science Foundation of China (61671113)
More Information
  • 摘要: 主动式毫米波阵列3维成像系统是人体安检成像系统的研究热点,该文对主动式毫米波阵列3维系统工作模式、信号模型和成像算法进行了介绍,并将深度学习中的卷积神经网络(CNN)热图检测方法和边框回归检测技术应用于人体安检成像异物检测。研究表明,基于热图的检测方法和基于YOLO的检测方法均可实现异物检测。基于热图的检测方法网络结构简单、易训练,但由于需要遍历整幅待检测图像,运算时间长,且生成的检测框尺寸固定,无法适应异物尺寸变化。基于YOLO的检测算法网络结构复杂、训练耗时长,但该方法在检测速度与检测框精度上优势明显,更利于机场安检等对实时性要求较高的检测应用。

     

  • 图  1  近场毫米波3维成像几何模型

    Figure  1.  The geometric model of near field millimeter wave 3D imaging

    图  2  2维成像结果

    Figure  2.  The 2D imaging result

    图  3  CNN结构

    Figure  3.  The CNN structure

    图  4  基于热图的目标检测结构

    Figure  4.  The target detection structure based on heat map

    图  5  YOLO的训练流程

    Figure  5.  The training process of YOLO

    图  6  非极大值抑制算法去除重复预测框

    Figure  6.  Removal of repeated prediction box by NMS algorithms

    图  7  原始实测成像图

    Figure  7.  The original measured image

    图  8  图像处理后成像图

    Figure  8.  The image after processing

    图  9  训练分类网络的样本

    Figure  9.  Samples of training classification network

    图  10  分类网络训练过程中平均损失和准确率

    Figure  10.  The average loss and accuracy in classification network training

    图  11  YOLO网络训练过程中的平均损失

    Figure  11.  The average loss in YOLO network training

    图  12  YOLO网络检测结果随图像尺寸变化情况

    Figure  12.  YOLO network detection results of different image size

    图  13  YOLO测试结果(不同训练次数)

    Figure  13.  YOLO test results (different training numbers)

    图  14  基于热图和YOLO检测结果

    Figure  14.  Test results based on heat map and YOLO

    表  1  YOLO网络检测结果(%)

    Table  1.   The YOLO network detection results (%)

    类名平均精确率(AP)
    gun95.43
    phone89.86
    knife90.88
    mAP92.06
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-10-22
  • 修回日期:  2019-07-02
  • 网络出版日期:  2019-10-01

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