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High-Performance Detection of Concealed Forbidden Objects on Human Body with Deep Neural Networks Based on Passive Millimeter Wave and Visible Imagery

  • Lin Guo
  • Shiyin QinEmail author
Article
  • 41 Downloads

Abstract

In this paper, a high-performance detection algorithm of concealed forbidden objects on human body is presented based on deep neural networks (DNN) and complementary advantages of passive millimeter wave imagery (PMMWI) and visible imagery (VI). With well capacity of penetrability, PMMWI can effectively reveal suspected forbidden objects concealed on human body without harm of ionizing radiation compared with conventional X-ray methods. However, due to its current limited imaging capability, the resolution of PMMWI is still unsatisfactory and easy to result in false alarms. Therefore, by complementarity of superiorities, VI is employed to overcome the deficiency of confusions. In this way, massive image samples of PMMWI and VI are simultaneously acquired and manually annotated as necessary training datasets to carry out deep learning on DNN models so as to achieve high-performance human body profile segmentation on both PMMWI and VI. Then, high-precision region registration of human body profiles is implemented between PMMWI and VI to localize and confirm high confident suspected targets and remove false alarm regions as well. According to the principle of synthetic integration and global optimization, a high performance detection algorithm system is constructed, analyzed, and assessed. A series of comprehensive experiment results demonstrate the outstanding performance of our proposed detection algorithm.

Keywords

Passive millimeter wave imagery Concealed forbidden object detection Deep neural networks Deep learning Semantic segmentation Security check 

Notes

Funding Information

This work was supported in part by the National Nature Science Foundation of China (Grant Nos. 61731001 and U1435220).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

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