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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Vydané v:Sensors (Basel, Switzerland) Ročník 20; číslo 6; s. 1678
Hlavní autori: Pang, Lei, Liu, Hui, Chen, Yang, Miao, Jungang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 17.03.2020
MDPI
Predmet:
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
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Popis
Shrnutí: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 weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20061678