Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE Asia-Pacific Microwave Conference, APMC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ434-436
ページ数3
ISBN(電子版)9781665437820
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Asia-Pacific Microwave Conference, APMC 2021 - Virtual, Online, Australia
継続期間: 2021 11月 282021 12月 1

出版物シリーズ

名前Asia-Pacific Microwave Conference Proceedings, APMC
2021-November

Conference

Conference2021 IEEE Asia-Pacific Microwave Conference, APMC 2021
国/地域Australia
CityVirtual, Online
Period21/11/2821/12/1

ASJC Scopus subject areas

  • 電子工学および電気工学

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