Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm

The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weap...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 20; no. 6; p. 1678
Main Authors:	Pang, Lei, Liu, Hui, Chen, Yang, Miao, Jungang
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 17.03.2020 MDPI
Subjects:	Accuracy Algorithms Computer Systems concealed object detection Crime - prevention & control Data collection Data processing Datasets Datasets as Topic Deep Learning Electromagnetic Phenomena Feasibility Studies Human body Humans Image Interpretation, Computer-Assisted - instrumentation Image Interpretation, Computer-Assisted - methods Limit of Detection Metals - analysis Methods neural network Neural networks Neural Networks, Computer passive millimeter wave real-time Safety Management Security Measures Sensitivity and Specificity Weapons yolov3 deep learning YOLOv3 real-time passive millimeter wave concealed object detection neural network
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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