Identification of Dangerous Goods in Human THZ Images

DOI

10.2991/ncce-18.2018.147How to use a DOI?

Keywords

signal-to-noise; RCNN; automatic and rapid; recognition rate; terahertz.

Abstract

For terahertz image with low signal-to-noise ratio, serious blur and poor resolution, this paper uses the mean filter to denoise the terahertz image, and then uses Faster RCNN algorithm to detect and identify the dangerous goods in the terahertz image. Different from traditional algorithms, Faster RCNN algorithm uses traditional detection algorithms to locate, segment, extract effective features, integrate detection and recognition, and achieve automatic and rapid detection of hidden objects in the human body. The experimental results show that the proposed algorithm can effectively identify the dangerous articles of controlled knives in terahertz images, and the recognition rate can reach 89.6%.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hong Xiao
AU  - Feng Zhu
PY  - 2018/05
DA  - 2018/05
TI  - Identification of Dangerous Goods in Human THZ Images
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 884
EP  - 888
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.147
DO  - 10.2991/ncce-18.2018.147
ID  - Xiao2018/05
ER  -