Radiometric Passive Imaging for Robust Concealed Object Identification

研究成果: Conference contribution

抄録

Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

本文言語English
ホスト出版物のタイトル2020 IEEE Radar Conference, RadarConf 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728189420
DOI
出版ステータスPublished - 2020 9月 21
イベント2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
継続期間: 2020 9月 212020 9月 25

出版物シリーズ

名前IEEE National Radar Conference - Proceedings
2020-September
ISSN(印刷版)1097-5659

Conference

Conference2020 IEEE Radar Conference, RadarConf 2020
国/地域Italy
CityFlorence
Period20/9/2120/9/25

ASJC Scopus subject areas

  • 電子工学および電気工学

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