Concealed weapon detection using microwave and millimeter wave sensors

Application of passive terahertz imaging in concealed weapon detection has been looked at, such that the final result is the segmentation of the foreground concealed weapons from the rest of the background. For the same, a fully automatic and completely generic technique, without any learning, has been proposed. It was observed that a simple thresholding step, exploiting varied intensity bands of the tetrahertz images is not enough. Thus, an innovative method to isolate humans and thus improve the region of interest (ROI) has been proposed. Thereafter, saliency has been used to further improve ROI, as these images are quite noisy and the central focusing aspect of saliency could handle the noise around the concealed weapons. It was observed that this step could handle the noise around the concealed weapons but degraded the boundaries of the concealed weapons. To further improve boundary adherence, superpixels are used. Finally, results are evaluated both quantitatively and qualitatively and outperformed the traditional approach.

Summary form only given. Multisensor information fusion is one of the key technologies required to make significant advances in information processing and utilization in a distributed information environment. The data fusion process model proposed by the US Joint Directors of Laboratories (JDL) is accepted widely for military applications. There have been other models such as a three level model proposed by the Defense Evaluation and Research Agency (DERA) of the United Kingdom. There is an ongoing research and development program in the area of concealed weapons detection sponsored by Air Force Research Laboratory (AFRL) and National Institute of Justice (NIJ). The main objectives of this program are twofold: development of efficient sensors and signal processing technologies for the detection of civilian applications. Data collected by different sensors needs to be combined intelligently to enhance detection performance for concealed weapons. Toward this goal we have developed several novel image/signal processing algorithms. These include algorithms for image registration, image enhancement, image fusion, denoising, and object extraction. While great strides in the CWD program have been made, its relationship with the data fusion process models has not been investigated. The goal of this presentation is to examine this relationship.

A breadboard ultrasound sensor was developed for remotely detecting and imaging concealed weapons. The breadboard sensor can detect metallic and non-metallic weapons concealed on a human body under heavy clothing at ranges up to 8 m and image the concealed weapons at ranges up to 5 m. This breadboard sensor has produced the only remote ultrasound images of concealed weapons ever published, including lexan (plastic) knives and a handgun concealed under a heavy sweatshirt at 15 feet. The remote imaging by ultrasound was made possible by several new technological developments. The sensor includes a novel, highly efficient source of high-power, tunable ultrasound radiation suitable for remote imaging in air. Together with millimeter-sized, highly sensitive ultrasound detectors and high-gain transceivers, these advances make possible the centimeter- resolution imaging of concealed weapons at ranges between 1 m and 5 m. The ultrasound images are processed by our IMAGE binary-thresholding program, which filters for noise, frequency, brightness, and contrast. To be developed is a brassboard sensor with an imaging array of ultrasound detectors, capable of real-time, video-frame-rate imaging of weapons concealed on moving humans.

In light of increasing interest in detection of concealed metallic weapons, there is a great need to have robust and non-obtrusive metal detection systems with large coverage areas. Conventional systems based on electromagnetic induction or X- rays are effective, but have small coverage areas in addition to requiring costly infrastructure. In this paper, we explore the use of ubiquitously present WiFi signals for non-obtrusive detection of concealed metal objects. For the purpose, we build a prototype system consisting of a single- antenna commodity WiFi radio as a transmitter, and two multi-antenna radios as receivers placed in an indoor environment of approximately 42 ft by 39 ft. We conduct extensive experiments with subjects walking through the setup with (or without) a sheet of metal placed around their chests. We use the channel state-information collected from the receivers to train a deep convolutional neural network, and find that the proposed system can differentiate between the metal and non-metal cases with an average accuracy of 86.44.

A novel cylindrical millimeter-wave imaging technique has been developed at the Pacific Northwest National Laboratory for the detection of metallic and non-metallic concealed weapons. This technique uses a vertical array of millimeter- wave antennas which is mechanically swept around a person in a cylindrical fashion. The wideband millimeter-wave data is mathematically reconstructed into a series of high- resolution images of the person being screened. Clothing is relatively transparent to millimeter-wave illumination,whereas the human body and concealed items are reflective at millimeter wavelengths. Differences in shape and reflectivity are revealed in the images and allow a human operator to detect and identify concealed weapons. A full 360 degree scan is necessary to fully inspect a person for concealed items. The millimeter-wave images can be formed into a video animation sequence in which the person appears to rotate in front of a fixed illumination source.This is s convenient method for presenting the 3D image data for analysis. This work has been fully sponsored by the FAA. An engineering prototype based on the cylindrical imaging technique is presently under development. The FAA is currently opposed to presenting the image data directly to the operator due to personal privacy concerns. A computer automated system is desired to address this problem by eliminating operator viewing of the imagery.

The radar's antenna usually determines the total size and weight of the radar since in most applications a narrow beam is required. Reduction in antenna size can be achieved without compromising its beam performance if its operating frequency were to be increased proportionally. Hence, if higher operating frequency is selected for a given radar application, then a smaller antenna can be designed, resulting in a more compact and light weight system; however, there are variations in the free-space path loss and the target radar cross section that need to be accounted for. For short range radar applications, such as vehicle collision avoidance, concealed weapons detection, target imaging, and aircraft assistive landing, millimeter-wave (MMW) radars (predominantly operating below 100 GHz) have become increasingly the systems of choice in these applications. Limitations in hardware performance in the upper portion of the millimeter-wave frequency band (between 200 GHz and 300 GHz) and the lack of phenomenological studies of terrain have kept these frequencies off limits for the aforementioned radar applications. Recent advancements in solid-state technologies have resulted in improved RF components operating in the upper millimeter-wave frequency range and into the terahertz domain. As a result there has been renewed interest in radar systems operating in the higher MMW frequencies. Unfortunately, the literature, with regard to the radar backscatter behavior of natural terrain at these frequencies, is rather limited in terms of target types, angular ranges, and polarizations. Knowledge of the expected radar return from natural and manmade targets is important for both modeling the targets and for the design of radar systems optimized for a specific application.

Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

Security has been a major concern in our societies due to the rise in crime rate, most especially in a crowded area. Concealed weapons have been posing a significant threat to government, law enforcement, security agencies, and civilians. Existing weapons detection systems seem to be not culpable of detecting concealed weapons without the cooperation of the person being searched. There remains a need for a weapons detector that can detect and identify concealed weapons for security enhancement in Nigeria. For this purpose, computer vision methods and a deep learning approach were applied for the identification of a weapon from captured images downloaded from the internet as a prototype for the study. Recent work in deep learning and machine learning using convolutional neural networks has shown considerable progress in the areas of object detection and recognition. The CNN algorithms are trained on the collected datasets to identify and differentiate between weapons and non-weapons. We built a concealed weapon detection system prototype and conducted a series of experiments to test the system's accuracy, precision, and false positives. The models were compared by evaluating their average values of sensitivity, specificity, F1 score, accuracy, and the area under the receiver operating characteristic curve (AUC). The experimental findings clearly demonstrated that the ResNet-50 model performed better than the VGG-16 and Alex Net models in terms of sensitivity, specificity, and accuracy.

In this day and age, increasingly easy access to firearms and other hand-held weapons has stirred up public violence concerns. Many of these weapons are comfortably concealed. The tremendous developments in technology can assure more security in public places by detecting such weapons in real-time and alerting the concerned authorities before any damage. In this paper, we propose using two state-of-the-art algorithms for performing real-time detection of concealed hand-held weapons. The algorithms used are Multi Contrast Convolutional Neural Networks (MC-CNN) and Faster Region-Based Convolutional Neural Networks (Faster R-CNN). Additionally, we present a comprehensive comparative analysis and evaluation of the Faster R-CNN and MC-CNN in detecting the weapons. This study has diverse industrial applications in real-time bank surveillance cameras and other public places, provided that they are under CCTV surveillance.

THz imaging, also known as terahertz imaging, is a relatively new technology that utilizes the terahertz frequency range of the electromagnetic spectrum to generate images of various materials and biological samples. This technology has promising applications in a wide range of biomedical fields, including cancer detection, imaging of biological tissues, and drug development. THz imaging has also applications in security sector, including explosives detection and as well as detection of concealed weapons. THz TPI imaging technique is relatively new but it has great applications in dentistry. The THz imaging has great potential in revealing cancerous tissue. Several studies have already been done by various scientists using THz imaging in the healthcare sector, specially detecting different types of cancers. THz imaging has some added advantages in comparison to the other types of imaging techniques availlable to the healthcare industry. Overall, this review article aims to provide an overview of the biomedical applications of THz imaging and its potential applications for future advancements in the field of medical sciences.

This paper discusses the current state-of-the-art in millimeter-wave radiometric imagers being developed for law enforcement use within the United States. The most prevalent application at present is the detection of concealed weapons (guns, knives, etc.) with a secondary application involving the detection of drugs and explosives. Three main topics will be addressed: the phenomenology of concealed weapon detection at millimeter wavelengths, the promise and short comings of first generation radiometric imagers, and the characteristics of second generation systems currently under development.

: This task provided for assembly, definition, and completion of technical enhancements in coordination with the National Law Enforcement and Corrections Technology Center -Northeast Region (NLECTC-NE). It also provided for Test and Evaluation (T&E) for the Advanced Generation of Interoperability for Law Enforcement (AGILE) and for analysis and assessment in cyber science including computer forensics. The mission of the NLECTC-NE in conjunction with the Air Force Research Laboratory/Information Directorate (AFRL/IF), is to facilitate the identification, development, and adoption of new products and technologies specifically designed for law enforcement, corrections, and other criminal justice applications. The current technology thrust areas for the NLECTC-NE are Concealed Weapons Detection (CWD), Secure Communications, Communications Interoperability, Timeline Analysis, Computer Forensics, Audio/Video Processing, Information Management, Automatic Speaker Recognition, Automatic Language Translation and Facial Recognition. This report outlines the major accomplishments of the task and identifies on-going technology efforts. These accomplishments include outreach activities, scientific and engineering assistance, AGILE development and technology demonstration activities, cyber crime initiatives, and Integrated Border Enforcement Teams (I BET) technology support.

Concealed weapon detection based on passive millimeter wave imaging requires a high spacial resolution to achieve useful output. In order to meet this requirement while decreasing the overall imaging time, the approach presented here employs a mechanically scanned focal plane array at 94 GHz. In this paper, details about a four-channel scanning radiometer setup are given using a novel compact W-band receiver. Finally, the measured data alongside with the required channel equalization and superresolution-based image enhancement are discussed.

Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual information measure (MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported methods, wavelet transform based image fusion and weighted average discrete wavelet transform based image fusion using genetic algorithm.

Microwave imaging technique allows obtaining images of hidden objects in structures and media using microwaves. This technique has various applications such as: nondestructive testing, medical imaging, concealed weapon detection, through-the-wall imaging, etc. Obtaining radar images in these applications is based on processing phase and amplitude of the reflected signal recorded over an aperture (a microwave hologram). Recently, systems began to appear in which the radar part is supplemented by an RGB-D sensor, which allows to obtain new capabilities. For example, there is the a microwave screening system architecture in which inverse synthetic aperture is formed by the natural motion of the subject in the vicinity of a stationary linear antenna array. The microwave system is complemented with an synchronous RGB-D video sensor which captures the trajectory of the moving subject in 3D and allows coherent processing of the radar signal. Another system detects objects buried under irregular surface and uses RGB-D sensor for capturing the surface relief for suppressing reflection of the sounding signal from the surface. Calibration between a radar and an RGB-D sensor is an essential process for microwave and optical data fusion. This article presents a novel approach for calibration, using a planar calibration target which is made of radiotransparent material (such a foam plastic sheet) with square marker and six small metal balls embedded in target surface and representing point objects. The proposed method exploits 3D-3D correspondences between coordinates of point objects in two coordinate systems associated to the sensor and to the radar. One points set is extracted from optical data, using marked corners of the target as a base points. Second points set is obtained from microwave data as local maxima of 3D volume of data reconstructed from one-frequency microwave hologram. Computer modeling were performed using Autodesk 3ds Max software by which models of all components of the system were built and the optical image from the sensor was modeled. Test experiments were carried out using a measurement system composed of the following components: a compact vector network analyzer (VNA), two mechanical scanners with stepper motors, one transmitting and one receiving horn antennas, mounted on the VNA, an RGB-D sensor, a microcontroller board, and a computer. The high accuracy of the method is confirmed both by computer modeling and physical experiment. The accuracy of determination of relative position between the radar and the sensor is about one fifth of the signal wavelength used.