基于轻量级U-Net深度学习的人体安检隐匿违禁物的实时检测  

Real-time Detection of Hiding Contraband in Human Body During the Security Check Based on Lightweight U-Net with Deep Learning

作　　者：李连伟 秦世引[1,2] LI Lianwei;QIN Shiyin(School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;School of Electrical Engineering&Intelligentization,Dongguan University of Technology,Dongguan 523808,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100191 [2]东莞理工学院电子工程与智能化学院,东莞523808

出　　处：《电子与信息学报》2022年第10期3435-3446,共12页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61731001)。

摘　　要：在高端智能安检系统研发中,如何使受检者在无接触正常行进过程中,对其实施人体是否携带隐匿违禁物的快速高效检测是具有挑战性的关键性技术。被动毫米波成像以其安全无害、穿透性强等突出优势而成为安检成像的热门选项。该文利用被动毫米波成像和可见光成像的优势互补,通过轻量级U-Net的深度学习,研究提出人体安检隐匿违禁物的高性能实时检测算法。首先构建和训练轻量级U-Net分割网络,进行被动毫米波图像(PMMWI)和可见光图像(VI)中人体轮廓的快速分割,实现人体与背景的有效分离,以获取疑似隐匿违禁物的轮廓信息。进而,以轻量级U-Net为工具,通过基于相似性测度的无监督学习方法进行被动毫米波人体轮廓图像与可见光人体轮廓图像的配准,以滤除虚警目标,并在可见光图像中进行疑似目标定位,得到单帧图像的检测结果。最后,通过序列多帧图像之检测结果的综合集成与推断,给出最终检测结果。通过在专门构建的数据集上的实验结果表明,该文所提方法的F1指标达到92.3%,展现出良好的性能优势。In the research and development of high-end intelligent security check system,it is a challenging key technology how to make the detection of whether the human body is carrying hiding contraband quickly and efficiently in the normal process of non-contact travel.Passive millimeter wave imaging has become a popular option for security imaging due to its outstanding advantages such as safety,harmlessness and strong penetration.In this paper,the complementary advantages of passive millimeter wave imaging and visible imaging are employed,and a high-performance detection algorithm for hiding contraband in human body based on the lightweight U-Net is proposed.A lightweight U-Net is first constructed and trained to realize the rapid segmentation of the human contour in Passive MilliMeter Wave Image(PMMWI)and Visible Image(VI).In this way,the information of human contour and hiding contraband can be extracted.Then,human contour registration on PMMWI/VI is realized by the unsupervised learning method based on the similarity measure with the lightweight U-Net.After filtering the false alarm target,the position of the hiding contraband is marked in VI and the detection result on single frame image can be obtained.In the end,the final detection result is given through the comprehensive integration and inference of the detection results of sequence multiframe images.Experimental results on a specially constructed dataset show that the proposed method reaches 92.3%of F1 evaluation index,thus demonstrates its performance advantages.

关 键 词：隐匿违禁物检测 人体安检系统 被动毫米波成像 人体轮廓分割 图像配准 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.4[电子电信—信息与通信工程]