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

LI Lianwei; QIN Shiyin

doi:10.11999/JEIT210787

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3435-3446

LI Lianwei, QIN Shiyin. Real-time Detection of Hiding Contraband in Human Body During the Security Check Based on Lightweight U-Net with Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3435-3446. doi: 10.11999/JEIT210787

Citation:

LI Lianwei, QIN Shiyin. Real-time Detection of Hiding Contraband in Human Body During the Security Check Based on Lightweight U-Net with Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3435-3446. doi: 10.11999/JEIT210787

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

doi: 10.11999/JEIT210787

LI Lianwei¹,
QIN Shiyin^{1, 2
,
,}

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2.
School of Electrical Engineering & Intelligentization, Dongguan University of Technology, Dongguan 523808, China

Funds: The National Natural Science Foundation of China (61731001)

Received Date: 2021-08-06
Accepted Date: 2021-12-13
Rev Recd Date: 2021-12-08

Available Online: 2021-12-25

Publish Date: 2022-10-10

Abstract

Abstract

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 multi-frame 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.
- Hiding contraband detection,
- Body security check system,
- Passive MilliMeter Wave Imaging (PMMWI),
- Human contour segmentation,
- Image registration

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References(22)

References

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