Classification of Metallic Targets Using a Walk-Through Metal Detection Portal

Metal detectors have been used for a long time for treasure hunting, security screening, and finding buried objects such as landmines or unexploded ordnance. Walk-through metal detection (WTMD) portals are used for making sure that forbidden or threatening metallic items, such as knives or guns, are not carried into secure areas at critical locations such as airports, court rooms, embassies, and prisons.The 9/11 terrorist act has given rise to stricter rules for aviation security worldwide, and the ensuing tighter security procedures have meant that passengers face more delays at airports. Moreover, the fear of terrorism has led to the adoption of security screening technology in a variety of places such as railway and coach stations, sports events, malls, and nightclubs.However, the current WTMD technology and scanning procedures at airports require that all metallic items be removed from clothing prior to scanning, causing inconvenience. Furthermore, alarms are triggered by innocuous items such as shoe shanks and artificial joints, along with overlooked items such as jewellery and belts. These lead to time- consuming, manual pat-down searches, which are found inconvenient, uncomfortable, and obtrusive by some.Modern WTMD portals are very sensitive devices that can detect items with only small amounts of metal, but they currently lack the ability to further classify the detected item. However, if a WTMD portal were able to classify objects reliably into, e.g., “knives”, “belts”, “keys”, the need for removing the items prior to screening would disappear, enabling a paradigm shift in the field of security screening.This thesis is based on novel research presented in five peer-reviewed publications. The scope of the problem has been narrowed down to a situation in which only one metallic item is carried through the portal at a time. However, the methods and results presented in this thesis can be generalized into a multi-object scenario. It has been shown that by using a WTMD portal and the magnetic polarisability tensor, it is possible to accurately distinguish between threatening and innocuous targets and to classify them into 10 to 13 arbitrary classes. Furthermore, a data library consisting of natural walk-throughs has been collected, and it has been demonstrated that the walk-through data collected with the above portal are subject to phenomena that might affect classification, in particular a bias and the so-called body effect. However, the publications show that, by using realistic walk-through data, high classification accuracy can be maintained regardless of the above problems. Furthermore, a self-diagnostics method for detecting unreliable samples has also been presented with potential to significantly increase classification accuracy and the reliability of decision making.The contributions presented in this thesis have a variety of implications in the field of WTMD-based security screening. The novel technology offers more information, such as an indication of the probable cause of the alarm, to support the conventional screening procedure. Moreover, eliminating the need for removing all metallic items prior to screening enables design of new products for scenarios such as sports events, where conventional screening procedures might be inconvenient, creating thus new business possibilities for WTMD manufacturing companies.The positive results give rise to a variety of future research topics such as using wideband data, enabling simultaneous classification of multiple objects, and developing the portal coil design to diminish signal nonlinearities. Furthermore, the ideas and the basic principles presented in this thesis may be applied to other metal detection applications, such as humanitarian demining

The Journal of Conventional Weapons Destruction, Issue 24.1 (2020)

Mine Action on the Korean Peninsula Raising the Profile of Mine Action A New Approach to IMAS Compliance Disposal of EO and Environmental Risk Mitigation Explosive Ordnance Risk Education - Measuring Behavior Chang

Vision-Based Remote Sensing Imagery Datasets From Benkovac Landmine Test Site Using An Autonomous Drone For Detecting Landmine Locations

Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV. Additionally, the related knowledge regarding the literature survey is presented to guide the researchers properly. More explicitly, two remote sensing modalities, namely, multispectral and long-wave infrared (LWIR) cameras were mounted on an advanced autonomous UAV and datasets were collected from a well-designed field containing various types of landmines. In this report, multispectral imagery and LWIR imagery datasets are presented for researchers who can fuse these datasets using their bespoke applications to increase the probability of detection, decrease the false alarm rate, and most importantly, improve their techniques based on the features of vision-based imagery datasets

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

A Multidisciplinary Analysis of Frequency Domain Metal Detectors for Humanitarian Demining

This thesis details an analysis of metal detectors (low frequency electromagnetic induction devices) with emphasis on Frequency Domain (FD) systems and the operational conditions of interest to humanitarian demining. After an initial look at humanitarian demining and a review of their basic principles we turn our attention to electromagnetic induction modelling and to analytical solutions to some basic FD direct (forward) problems. The second half of the thesis focuses then on the analysis of an extensive amount of experimental data. The possibility of target classification is first discussed on a qualitative basis, then quantitatively. Finally, we discuss shape and size determination via near field imaging

Department of Defense Dictionary of Military and Associated Terms

The Joint Publication 1-02, Department of Defense Dictionary of Military and Associated Terms sets forth standard US military and associated terminology to encompass the joint activity of the Armed Forces of the United States. These military and associated terms, together with their definitions, constitute approved Department of Defense (DOD) terminology for general use by all DOD components

Signal Processing Techniques for Landmine Detection Using Impulse Ground Penetrating Radar (ImGPR)

Landmines and unexploded ordinance (UXO) are laid during a conflict against enemy forces. However, they kill or maim civilians decades after the conflict has ended. There are more than 110 million landmines actively lodged in the globe. Every year more than 26,000 innocent civilians are killed or maimed. Most modern landmines are mainly nonmetallic or plastic, which are difficult to be detected using conventional metal detectors. Detection using hand-held prodding is a slow and expensive process. Impulse Ground Penetrating Radar (ImGPR) is a nondestructive technique capable of detecting shallowly buried nonmetallic anti-personnel (AP) and anti-tank (AT) landmines. In this PhD thesis, ImGPR is considered as a tool to detect landmines and UXO. The presence of strong ground clutter and noise degrade the performance of GPR. Hence, using a GPR sensor is almost impossible without the application of sophisticated signal processing. In electromagnetic wave propagation modeling, a multilayer transmission line technique is applied. It considers different soil types at different moisture levels. Plastic targets of different diameters are buried at different depths. The modeled signal is then used to estimate the ground and buried target parameters. In a parameter estimation procedure, a surface reflection parameter method (SRPM) is applied. Signal processing algorithms are implemented for clutter reduction and decision making purposes. Attention is mainly given to the development of techniques, that are applicable to real-time landmine detection. Advanced techniques are preceded by elementary preprocessing techniques, which are useful for signal correction and noise reduction. Background subtraction techniques based on multilayer modeling, spatial filtering and adaptive background subtraction are implemented. In addition to that, decorrelation and symmetry filtering techniques are also investigated. In the correlated decision fusion framework, local decisions are transmitted to the fusion center so as to compute a global decision. In this case, the concept of confidence information of local decisions is crucial to obtain acceptable detection results. The Bahadur-Lazarsfeld and Chow expansions are used to estimate the joint probability density function of the correlated decisions. Furthermore, a decision fusion based on fuzzy set is implemented. All proposed methods are evaluated using simulated as well as real GPR data measurements of many scenarios. The real data collection campaign took place at the Griesheim old airport and Botanischer Garten, Darmstadt, Germany in July 2011

Defense Technical Information Center thesaurus

This DTIC Thesaurus provides a basic multidisciplinary subject term vocabulary used by DTIC to index and retrieve scientific and technical information from its various data bases and to aid DTIC`s users in their information storage and retrieval operations. It includes an alphabetical posting term display, a hierarchy display, and a Keywork Out of Context (KWOC) display

Sea Mines and Countermeasures: A Bibliography

This compilation was prepared for the Dudley Knox Library, Naval Postgraduate School, Monterey, CA