Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer

Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW images, PMMW hidden object detection based on deep learning usually suffers from low accuracy and low classification confidence. To tackle the above problems, this paper proposes a Task-Aligned Detection Transformer network, named PMMW-DETR. In the first stage, a Denoising Coarse-to-Fine Transformer (DCFT) backbone is designed to extract long- and short-range features in the different scales. In the second stage, we propose the Query Selection module to introduce learned spatial features into the network as prior knowledge, which enhances the semantic perception capability of the network. In the third stage, aiming to improve the classification performance, we perform a Task-Aligned Dual-Head block to decouple the classification and regression tasks. Based on our self-developed PMMW security screening dataset, experimental results including comparison with State-Of-The-Art (SOTA) methods and ablation study demonstrate that the PMMW-DETR obtains higher accuracy and classification confidence than previous works, and exhibits robustness to the PMMW images of low quality

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

An electromagnetic imaging system for metallic object detection and classification

PhD ThesisElectromagnetic imaging currently plays a vital role in various disciplines, from engineering to medical applications and is based upon the characteristics of electromagnetic fields and their interaction with the properties of materials. The detection and characterisation of metallic objects which pose a threat to safety is of great interest in relation to public and homeland security worldwide. Inspections are conducted under the prerequisite that is divested of all metallic objects. These inspection conditions are problematic in terms of the disruption of the movement of people and produce a soft target for terrorist attack. Thus, there is a need for a new generation of detection systems and information technologies which can provide an enhanced characterisation and discrimination capabilities. This thesis proposes an automatic metallic object detection and classification system. Two related topics have been addressed: to design and implement a new metallic object detection system; and to develop an appropriate signal processing algorithm to classify the targeted signatures. The new detection system uses an array of sensors in conjunction with pulsed excitation. The contributions of this research can be summarised as follows: (1) investigating the possibility of using magneto-resistance sensors for metallic object detection; (2) evaluating the proposed system by generating a database consisting of 12 real handguns with more than 20 objects used in daily life; (3) extracted features from the system outcomes using four feature categories referring to the objects’ shape, material composition, time-frequency signal analysis and transient pulse response; and (4) applying two classification methods to classify the objects into threats and non-threats, giving a successful classification rate of more than 92% using the feature combination and classification framework of the new system. The study concludes that novel magnetic field imaging system and their signal outputs can be used to detect, identify and classify metallic objects. In comparison with conventional induction-based walk-through metal detectors, the magneto-resistance sensor array-based system shows great potential for object identification and discrimination. This novel system design and signal processing achievement may be able to produce significant improvements in automatic threat object detection and classification applications.Iraqi Cultural Attaché, Londo

Terahertz imaging and spectroscopy : application to defense and security

The aim of this work is to demonstrate the potential and capabilities of terahertz technology for parcels screening and inspection to detect threats such as weapons and explosives, without the need to open the parcel.In this study, we first present terahertz time-domain spectroscopy and spectral imaging for explosives detection. Two types of explosives as well as their binary mixture is analyzed. Due to the complexity of extracting information when facing such mixtures of samples, three chemometric tools are used: principal component analysis (PCA), partial least square analysis (PLS) and partial least squares-discriminant analysis (PLS-DA). The analyses are applied to terahertz spectral data and to spectral-images in order to: (i) describe a set of unknown data and identify similarities between samples by PCA; (ii) create a classification model and predict the belonging of unknown samples to each of the classes, by PLS-DA; (iii) create a model able to quantify and predict the explosive concentrations in a pure state or in mixtures, by PLS.The second part of this work focuses on millimeter wave imaging for weapon detection in parcels. Three different imaging techniques are studied: passive imaging, continuous wave (CW) active imaging and frequency modulated continuous wave (FMCW) active imaging. The performances, the advantages and the limitations of each of the three techniques, for parcel inspection, are exhibited. Moreover, computed tomography is applied to each of the three techniques to visualize data in 3D and inspect parcels in volume. Thus, a special tomography algorithm is developed by taking in consideration the Gaussian propagation of the wave.Le but de ce travail est de quantifier le potentiel et les capacités de la technologie térahertz à contrôler des colis afin de détecter les menaces telles que les armes et les explosifs, sans avoir besoin d'ouvrir le colis.Dans cette étude, nous présentons la spectroscopie térahertz résolue en temps et l'imagerie multi-spectrale pour la détection des explosifs. Deux types d’explosifs, ainsi que leurs mélanges binaires sont analysés. En raison de la complexité de l'extraction des informations face à tels échantillons, trois outils de chimiométrie sont utilisés: l’analyse en composantes principales (ACP), l'analyse des moindres carrés partiels (PLS) et l'analyse des moindres carrés partiels discriminante (PLS-DA). Les méthodes sont appliquées sur des données spectrales térahertz et sur des images spectrales pour : (i) décrire un ensemble de données inconnues et identifier des similitudes entre les échantillons par l'ACP ; (ii) créer des classes, ensuite classer les échantillons inconnus par PLS-DA ; (iii) créer un modèle capable de prédire les concentrations d’un explosif, à l'état pur ou dans des mélanges, par PLS.Dans la deuxième partie de ce travail, nous présentons l'imagerie par les ondes millimétriques pour la détection d'armes dans les colis. Trois techniques d'imagerie différentes sont étudiées : l'imagerie passive, l’imagerie active par des ondes continues (CW) et l’imagerie active par modulation de fréquence (FMCW). Les performances, les avantages et les limitations de chacune de ces techniques, pour l’inspection de colis, sont présentés. En outre, la technique de reconstruction tomographique est appliquée à chacune de ces trois techniques, pour visualiser en 3D et inspecter les colis en volume. Dans cet ordre, un algorithme de tomographie spécial est développé en prenant en considération la propagation gaussienne de l'onde

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect’s body despite concealment

Remote Detection of Concealed Guns and Explosives

A reliable method of remotely detecting concealed guns and explosives attached to the human body is of great interest to governments and security forces throughout the world. This thesis describes the development and trials of a new remote non-imaging concealed threat detection method using active millimetre wave radar using the microwave and mmwave frequencies bands 14 – 40 and 75 – 110 GHz (Ku, K, Ka and W). The method is capable of not only screening for concealed objects, like the current generation of concealed object detectors, but also of differentiating between mundane and threat objects. The areas focused upon during this investigation were: identifying the impact of different commonly worn fabrics as barriers to detection; consulting with end users about their requirements and operational needs; a comparison of different frequency bands for the detection of guns and explosives; exploring the effects of polarisation on object detection; a performance comparison of different detection schemes using Artificial Neural Networks; improving existing data acquisition systems and prototyping of a real-time capture system

Concealed Explosives Detection using Swept Millimetre Waves

The aim of this project is to develop a system for the stand-o detection (typically ten metres) of concealed body-worn explosives. The system must be capable of detecting a layer of explosive material hidden under clothing and distinguishing explosives from everyday objects. Millimetre wave radar is suitable for this application. Millimetre Waves are suitable because they are not signi cantly attenuated by atmospheric con- ditions and clothing textiles are practically transparent to this radiation. Detection of explosive layers from a few mm in thickness to a few cm thickness is required. A quasi optical focussing element is required to provide su cient antenna directivity to form a narrow, highly directional beam of millimetre waves, which can be directed and scanned over the person being observed. A system of antennae and focussing optics has been modelled and built using designs from nite element analysis (FEA) software. Using the developed system, represen- tative data sets have been acquired using a Vector Network Analyser (VNA) to act as transmitter and receiver, with the data saved for processing at a later time. A novel data analysis algorithm using Matlab has been developed to carry out Fourier Transforms of the data and then perform pattern matching techniques using arti cial neural networks (ANN's). New ways of aligning and sorting data have been found using cross-correlation to order the data by similar data slices and then sorting the data by amplitude to take the strongest 50% of data sets. The signi cant contribution to knowledge of this project will be a system which can be eld tested and which will detect a layer of dielectric at a stando distance, typically of ten metres, and signal processing algorithms which can recognise the di erence 17 between the response of threat and non-threat objects. This has partially been achieved by the development of focussing optics to acquire data sets which have then been aligned by cross-correlation, sorted and then used to train a pattern matching technique using neural networks. This technique has shown good results in di erentiating between a person wearing simulated explosives and a person not carrying simulated explosives. Further work for this project includes acquiring more data sets of everyday objects and training the neural network to distinguish between threat objects and non-threat objects. The operational range also needs increasing using either a larger aperture optical element or a similarly sized Cassegrain antenna. The system needs adapting for real time use with the data processing techniques developed in Matlab. The VNA is operated over a band of 14 to 40 GHz, future work includes moving to a stand-alone transmitter and receiver operating at w-band (75 to 110 GHz)

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with