Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging

San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Kazuhiko Tamesue, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In application related to public security check system, passive and active imaging of millimeter wave still faces critical challenges in providing high resolution quality images. Improving the detection, localization, and recognition accuracy of concealed object detection systems is very challenging due to the lack of a dataset of millimeter wave images with good resolution. Although previous studies proposed artificial intelligence-based concealed object recognition systems, improving accuracy remains a critical challenge. Therefore, in this paper, we propose two kinds of training dataset generation methods based on the proposed active millimeter wave imaging (AMWI) approaches presented in our previous work to improve the accuracy of convolutional neural networks (CNN)-based concealed object recognition systems. First, a depth-based training dataset generation method and a distance-based training dataset generation method are proposed for specular images and nonspecular images. Finally, a CNN-based concealed object recognition system is proposed by using generated active millimeter wave images and interferometer active images to improve the recognition accuracy.

Original languageEnglish
Title of host publication2021 IEEE Asia-Pacific Microwave Conference, APMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages3
ISBN (Electronic)9781665437820
Publication statusPublished - 2021
Event2021 IEEE Asia-Pacific Microwave Conference, APMC 2021 - Virtual, Online, Australia
Duration: 2021 Nov 282021 Dec 1

Publication series

NameAsia-Pacific Microwave Conference Proceedings, APMC


Conference2021 IEEE Asia-Pacific Microwave Conference, APMC 2021
CityVirtual, Online


  • Active Imaging
  • Deep Learning
  • Millimeter Wave

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging'. Together they form a unique fingerprint.

Cite this