留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

PMMWI与Ⅵ优势互补的人体隐蔽违禁物检测与定位

赵国 秦世引

赵国, 秦世引. PMMWI与Ⅵ优势互补的人体隐蔽违禁物检测与定位[J]. 北京航空航天大学学报, 2019, 45(10): 2011-2025. doi: 10.13700/j.bh.1001-5965.2019.0019
引用本文: 赵国, 秦世引. PMMWI与Ⅵ优势互补的人体隐蔽违禁物检测与定位[J]. 北京航空航天大学学报, 2019, 45(10): 2011-2025. doi: 10.13700/j.bh.1001-5965.2019.0019
ZHAO Guo, QIN Shiyin. Detection and localization of concealed forbidden objects on human body based on complementary advantages of PMMWI and Ⅵ[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 2011-2025. doi: 10.13700/j.bh.1001-5965.2019.0019(in Chinese)
Citation: ZHAO Guo, QIN Shiyin. Detection and localization of concealed forbidden objects on human body based on complementary advantages of PMMWI and Ⅵ[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 2011-2025. doi: 10.13700/j.bh.1001-5965.2019.0019(in Chinese)

PMMWI与Ⅵ优势互补的人体隐蔽违禁物检测与定位

doi: 10.13700/j.bh.1001-5965.2019.0019
基金项目: 

国家自然科学基金 61731001

详细信息
    作者简介:

    赵国  男, 博士研究生。主要研究方向:深度学习、图像语义分割、目标的检测与识别

    秦世引  男, 博士, 教授, 博士生导师。主要研究方向:图像处理、模式识别、智能优化控制等

    通讯作者:

    秦世引, E-mail: qsy@buaa.edu.cn

  • 中图分类号: TP391

Detection and localization of concealed forbidden objects on human body based on complementary advantages of PMMWI and Ⅵ

Funds: 

National Natural Science Foundation of China 61731001

More Information
  • 摘要:

    根据公共场所人体安检的性能要求和技术需求,将被动毫米波成像(PMMWI)的可透视成像性能优势与可见光成像(Ⅵ)的细节高分辨性能优势相结合,提出一种基于PMMWI与Ⅵ优势互补的人体隐蔽违禁物检测与定位算法。首先,提出一种基于低层特征融合的改进U-Net以增强深度神经网络(DNN)对PMMWI中弱小目标轮廓的敏感度,提高PMMWI中人体轮廓和隐蔽违禁物的分割精度,并同时实现Ⅵ中人体轮廓的像素级分割;然后,在PMMWI和Ⅵ中的人体轮廓分割基础上,通过基于人体轮廓的尺度变换与滑动适配实现PMMWI人体轮廓和Ⅵ人体轮廓的良好配准,根据配准结果实现单帧图像中人体隐蔽违禁物的高效检测;最后,通过序列图像检测结果的对比融合与优化决策给出隐蔽违禁物的定位结果。一系列综合实验与对比分析结果,验证了提出的人体隐蔽违禁物检测与定位算法的性能优势。

     

  • 图 1  主/被动毫米波成像安检设备示例

    Figure 1.  Illustration for AMMWI and PMMWI security check equipment

    图 2  主/被动毫米波成像结合互补的安检方案

    Figure 2.  Security check scheme based on combination and complementation of AMMWI and PMMWI

    图 3  PMMWI和Ⅵ示例

    Figure 3.  Samples of PMMWI and Ⅵ

    图 4  基于PMMWI和Ⅵ优势互补的检测与定位方案

    Figure 4.  Detection and localization scheme based on complementary advantage of PMMWI and Ⅵ

    图 5  基于PMMWI和Ⅵ优势互补的隐蔽违禁物检测与定位系统的组织结构与核心模块

    Figure 5.  Organization structure and core modules of concealed forbidden object detection and localization system based on complementary advantage of PMMWI and Ⅵ

    图 6  U-Net结构[14]

    Figure 6.  U-Net architecture[14]

    图 7  1×1卷积特征的拼接

    Figure 7.  Concatenation of features by 1×1 convolution

    图 8  基于低层特征融合的LFF-UNet网络结构

    Figure 8.  LFF-UNet architecture based on low-layer feature fusion

    图 9  本文提出的3种LFFB结构

    Figure 9.  Three types of LFFB structure proposed in our work

    图 10  PMMWI和Ⅵ样本数据集与人工标注图像

    Figure 10.  Dataset and manually annotated image of PMMWI and Ⅵ samples

    图 11  PMMWI中人体轮廓分割DNN模型性能进化曲线

    Figure 11.  Performance evolution curves of human contour segmentation DNN models in PMMWI

    图 12  基于LFF-UNet的PMMWI人体轮廓分割算法

    Figure 12.  Segmentation algorithm of human contour in PMMWI based on LFF-UNet architecture

    图 13  不同DNN模型PMMWI人体轮廓分割对比

    Figure 13.  Comparison of human contour segmentation in PMMWI among different DNN models

    图 14  不同DNN模型分割结果IoU对比

    Figure 14.  Comparison of IoU of segmentation result among different DNN models

    图 15  不同距离下的Ⅵ和PMMWI人体轮廓分割对比

    Figure 15.  Comparison of segmentation of human contours between Ⅵ and PMMWI in different distances

    图 16  PMMWI与Ⅵ人体轮廓尺度变换与滑动适配

    Figure 16.  Scale transform and sliding fit of human contour in PMMWI and Ⅵ

    图 17  PMMWI和Ⅵ匹配过程展示

    Figure 17.  Exhibition of image registration process of PMMWI and Ⅵ

    图 18  基于人体轮廓配准的隐蔽违禁物检测与定位

    Figure 18.  Detection and localization of concealed forbidden object based on human contour registration

    图 19  人体隐蔽违禁物检测与定位结果

    Figure 19.  Results of detection and localization of concealed forbidden objects on human body

    图 20  多帧人体隐蔽违禁物检测结果对比

    Figure 20.  Comparison of multi-frame detection results of concealed forbidden objects on human body

    图 21  基于检测结果对比融合的复合定位

    Figure 21.  Composite localization based on contrasting fusion of detection results

    图 22  算法与模型的综合集成与优化

    Figure 22.  Comprehensive integration and optimization of algorithms and models

    图 23  远距离人体隐蔽违禁物检测

    Figure 23.  Detection of concealed forbidden objectson human body from far distance

    图 24  近距离人体隐蔽违禁物检测

    Figure 24.  Detection of concealed forbidden objectson human body from close distance

    图 25  人体隐蔽违禁物定位误差对比

    Figure 25.  Comparison of localization error of concealed forbidden objects on human body

    图 26  人体隐蔽违禁物定位可视化结果

    Figure 26.  Visualization of localization result for concealed forbidden objects on human body

    图 27  U-Net与LFF3-UNet网络跨越连接层特征图可视化对比

    Figure 27.  Comparison of feature map visualization at skip-connection layer between U-Net and LFF3-UNet

    表  1  人体隐蔽违禁物检测IoU对比

    Table  1.   Comparison of IoU for detection of concealed forbidden objects on human body

    检测方式 a b c d e
    远距离检测 0.642 0.859 0.782 0.490 0.648
    近距离检测 0.786 0.810 0.362 0.584 0.853
    下载: 导出CSV
  • [1] 王任肩, 王宇光, 陈志福, 等.大型活动中的安检排爆工作及技术保障研究[J].警察技术, 2018(5):19-22. doi: 10.3969/j.issn.1009-9875.2018.05.004

    WANG R J, WANG Y G, CHEN Z F, et al.Research on exploder clearing work and technical support in security inspection of large activities[J].Police Technology, 2018(5):19-22(in Chinese). doi: 10.3969/j.issn.1009-9875.2018.05.004
    [2] 费鹏, 方维海, 温鑫, 等.用于人员安检的主动毫米波成像技术现状与展望[J].微波学报, 2015, 31(2):91-96. http://d.old.wanfangdata.com.cn/Periodical/wbxb201502020

    FEI P, FANG W H, WEN X, et al.State of the art and future prospect of the active millimeter wave imaging technique for personnel screening[J].Journal of Microwaves, 2015, 31(2):91-96(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/wbxb201502020
    [3] GROSSMAN E N, MILLER A J.Active millimeter-wave imaging for concealed weapons detection[C]//Proceedings of Conference on Passive Millimeter-Wave Imaging Technology Ⅵ and Radar International Society for Optics and Photonics.Bellingham: SPIE, 2003, 5077: 62-70.
    [4] ANDREWS D A, HARMER S W, BOWRING N J, et al.Active millimeter wave sensor for standoff concealed threat detection[J].IEEE Sensors Journal, 2013, 13(12):4948-4954. doi: 10.1109/JSEN.2013.2273487
    [5] DU K, ZHANG L, CHEN W, et al.Concealed objects detection based on FWT in active millimeter-wave images[C]//Proceedings of 7th International Conference on Electronics and Information Engineering.Bellingham: SPIE, 2017, 10322: 103221O.
    [6] REN S, HE K, GIRSHICK R, et al.Faster R-CNN:Towards real-time object detection with region proposal networks[J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2017, 39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031
    [7] 骆尚, 吴晓峰, 杨明辉, 等.基于卷积神经网络的毫米波图像人体隐匿物检测[J].复旦学报(自然科学版), 2018, 57(4):442-452. http://d.old.wanfangdata.com.cn/Periodical/fdxb201804005

    LUO S, WU X F, YANG M H, et al.Convolutional-neural-network-based human concealed object detection for millimeter wave images[J].Journal of Fudan University(Natural Science), 2018, 57(4):442-452(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/fdxb201804005
    [8] SINCLAIR G N, ANDERTON R N, APPLEBY R.Passive millimeter-wave concealed weapon detection[C]//Proceedings of Enabling Technologies for Law Enforcement and Security.Bellingham: SPIE, 2001, 4232: 142-152.
    [9] YEOM S, LEE D, SON J, et al.Real-time outdoor concealed-object detection with passive millimeter wave imaging[J].Optics Express, 2011, 19(3):2530-2536. doi: 10.1364/OE.19.002530
    [10] YEOM S, LEE D, JANG Y, et al.Real-time concealed-object detection and recognition with passive millimeter wave imaging[J].Optics Express, 2012, 20(9):9371-9381. doi: 10.1364/OE.20.009371
    [11] 周健, 叶金晶, 孙谦晨, 等.主动毫米波成像性别识别算法研究[J].红外, 2018, 39(9):34-40. doi: 10.3969/j.issn.1672-8785.2018.09.006

    ZHOU J, YE J J, SUN Q C, et al.Study of a gender identification algorithm for active millimeter-wave imaging[J].Infrared, 2018, 39(9):34-40(in Chinese). doi: 10.3969/j.issn.1672-8785.2018.09.006
    [12] SHELHAMER E, LONG J, DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651. doi: 10.1109/TPAMI.2016.2572683
    [13] BADRINARAYANAN V, KENDALL A, CIPOLLA R.SegNet:A deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12):2481-2495. doi: 10.1109/TPAMI.2016.2644615
    [14] RONNEBERGER O, FISCHER P, BROX T.U-Net: Convolutional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin: Springer, 2015: 234-241.
    [15] 桑伟, 岳胜利.毫米波成像技术在人体安全检查领域的应用[J].中国安防, 2013(4):83-87. doi: 10.3969/j.issn.1673-7873.2013.04.018

    SANG W, YUE S L.Application of millimeter wave imaging technology on the area of human body security inspection[J].China Security & Protection, 2013(4):83-87(in Chinese). doi: 10.3969/j.issn.1673-7873.2013.04.018
    [16] IGLOVIKOV V, SHVETS A.TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation[D].(2018-01-17)[2018-12-20].https://arxiv.org/abs/1801.05746. https://arxiv.org/abs/1801.05746
    [17] LIN M, CHEN Q, YAN S C.Network in network[D].(2014-03-04)[2018-12-20].https://arxiv.org/abs/1312.4400.
    [18] SZEGEDY C, LIU W, JIA Y, et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2015: 1-9.
    [19] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision[D].(2015-12-11)[2018-12-20].https://arxiv.org/abs/1512.00567v2.
    [20] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[D].(2015-12-10)[2018-12-20].https://arxiv.org//abs/1512.03385.
    [21] XIE S, GIRSHICK R, DOLLÁR P, et al.Aggregated residual transformations for deep neural networks[D].(2017-04-11)[2018-12-20].https://arxiv.org/abs/1611.05431.
    [22] KINGMA D P, BA J L.Adam: A method for stochastic optimization[D].(2017-01-30)[2018-12-20].https://arxiv.org/abs/1412.6980.
    [23] ZEILER M D, FERGUS R.Visualizing and understanding convolutional networks[C]//Proceedings of European Conference on Computer Vision.Berlin: Springer, 2014: 818-833.
  • 加载中
图(27) / 表(1)
计量
  • 文章访问数:  457
  • HTML全文浏览量:  11
  • PDF下载量:  511
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-01-17
  • 录用日期:  2019-05-28
  • 刊出日期:  2019-10-20

目录

    /

    返回文章
    返回
    常见问答