Combining Commercially Available Active and Passive Sensors Into a Millimeter-Wave Imager for Concealed Weapon Detection

García-Rial, Federico; Montesano, Daniel; Gomez, Ignacio; Callejero, Carlos; Bazus, Francis; Grajal, Jesús

doi:10.1109/tmtt.2018.2880757

Cited by 42 publications

(14 citation statements)

References 39 publications

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“…And there are three grid sizes of 13, 26, and 52 for each picture. So, the parameters selections of those layers indicate to use these three grid scales of each picture, and each grid contains 21 predictions (include bounding boxes and category probability of two objects) defined as in equation (1). In this experiment, the input for the YOLOv3 network is a 416 × 416 × 3 tensor, and it can output three sensors in scales 13 × 13 × 21, 26 × 26 × 21, and 52 × 52 × 21 respectively, by adding a passthrough layer and an upsampling layer to the target detection process.…”

Section: Yolov3 Network Architecturementioning

confidence: 99%

“…The detection of concealed targets under people's clothing is essential for public security. With the unique advantage of penetrating most materials, except for metal and water, millimeter wave imaging systems have been employed for concealed target visualization without privacy concerns [1]. Different from active modes, the millimeter sensor usually relies on its strong penetrability, and targets can be identified through their naturally emitted and reflected radiations, detected by using a Passive Millimeter Wave imaging system [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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

Pang

Liu

Chen

et al. 2020

Sensors

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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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Section: Yolov3 Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pang

Liu

Chen

et al. 2020

Sensors

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“…THz applications include security screening (García‐rial et al., ; Kaltenecker et al., ; (Liu, Zhong, Karpowicz, Chen, & Zhang, ), astronomical studies (Leisawitz et al., ), communication (Akyildiz, Miquel, & Han, ; Ergün & Sönmez, , nondestructive testing (Probst, Scheller, & Koch, ; Rutz, Koch, Khare, & Moneke, ), medical imaging (Bowman et al., ; Chavez, Bowman, Wu, Bailey, & El‐Shenawee, ; Dougherty et al., ; Fitzgerald et al., ; Sun et al., ; Truong, Fitzgerald, Fan, & Wallace, ), imaging of dental caries (Karagoz, Altan, & Kamburoglu, ; Ripoche, Reynard, Narcisse, & Roberts, ), pharmaceuticals (Markl, Dong, Li, & Zeitler, ; Pei et al., ; Sibik & Zeitler, ; Warnecke et al., , agricultural products (Liu et al., ; Liu, Liu, Hu, Yang, & Zheng, ; Luo, Zhu, Xu, & Cui, ; Nie, Qu, Lin, Dong, & He, ; Ogawa et al., ; Qin, Li, Chen, & Chen, ), food inspection and quality control (Ge, Jiang, Lian, & Zhang, ; Huang et al., , Shin, Choi, & Ok, ; Ok, Park, Kim, Chun, & Choi, ; Ok, Park, Lim, Jang, & Choi, ; Wang, Sun, & Pu, ; Yan, Liu, Qu, & Liu, ), environmental monitoring (Bigourd et al., ), and biosensing and label‐free monitoring of various biological processes (Yang et al., ). Full body THz scanners are installed in many airport checkpoints for detection of concealed objects and explosives.…”

Section: Introductionmentioning

confidence: 99%

“…THz applications include security screening (García-rial et al, 2018;Kaltenecker et al, 2018;(Liu, Zhong, Karpowicz, Chen, & Zhang, 2007), astronomical studies (Leisawitz et al, 2000), communication (Akyildiz, Miquel, & Han, 2014;Ergün & Sönmez, 2015, nondestructive testing (Probst, Scheller, & Koch, 2011;Rutz, Koch, Khare, & Moneke, 2006), medical imaging Chavez, Bowman, Wu, Bailey, & El-Shenawee, 2018;Dougherty et al, 2007;Fitzgerald et al, 2006;Sun et al, 2017;Truong, Fitzgerald, Fan, & Wallace, 2018), imaging of dental caries (Karagoz, Altan, & Kamburoglu, 2015;Ripoche, Reynard, Food applications of THz spectroscopy and imaging . .…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Afsah‐Hejri

Hajeb

Ara

et al. 2019

Comp Rev Food Sci Food Safe

211

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Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory‐scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

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“…For example, X-rays harm the human body, arched metal detectors only discern metal objects, and manual inspection poses the risk of personal discomfort. The millimeter-wave (MMW) three-dimensional imaging scanner [ 2 , 3 , 4 , 5 , 6 , 7 ] based on the near-field synthetic aperture radar (NF-SAR) three-dimensional imaging technology [ 8 , 9 , 10 , 11 ] offers an alternative. When compared with the traditional security-check measures, the MMW three-dimensional imaging scanner can provide the following advantages: 1.…”

Section: Introductionmentioning

confidence: 99%

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

Meng

Zhang

Wang

2020

Sensors

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Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount and complete sample sets of objects. In this paper, a new algorithm is developed using the human pose segmentation followed by the deep CNN detection. The algorithm is emphasized to learn the similarity with humans’ body clutter applied to training corresponding CNNs after the image segmentation base of the pose estimation. Moreover, the suspicious object recognition in the MMW image is converted to a binary classification task. Instead of recognizing all sorts of suspicious objects, the CNN detector determines whether the body part images present the abnormal patterns containing suspicious objects. The proposed algorithm that is based on CNN with the pose segmentation has concise configuration, but optimal performance in the suspicious object detection. Extensive experiments confirm the effectiveness and superiority of the proposal.

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Combining Commercially Available Active and Passive Sensors Into a Millimeter-Wave Imager for Concealed Weapon Detection

Cited by 42 publications

References 39 publications

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

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

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

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