Landmine Detection Research Articles

DL-Based Clutter Removal in Migrated GPR Data for Detection of Buried Target

As a nondestructive and nonintrusive geophysical electromagnetic technology, ground-penetrating radar (GPR) has been widely applied to subsurface target detection, such as landmine detection, pipeline detection, and underground cavity detection. The target response received by the GPR system is generally contaminated by clutter, which greatly affects the detection performance of the buried targets. In this letter, a novel clutter removal method combining migration and dictionary learning (DL) is presented. First, the proposed method applies the frequency–wavenumber (F–K) migration to the received GPR B-scan data. Then, since the focused target response and the clutter in the migrated GPR B-scan data present different morphological components, DL can be applied to the migrated GPR B-scan data to separate the focused target response (point-shaped structure) from the clutter (horizontal strip-shaped structure). Both numerical simulated data and experimental data collected by a real GPR system are used to evaluate the performance of the proposed clutter removal method. The experimental results demonstrate the effectiveness of the proposed clutter removal method under irregular clutter conditions, which improves the detection ability of the buried targets.

UAV-Borne FMCW InSAR for Focusing Buried Objects

Antipersonnel mines are hidden weapons that are usually buried close to the surface and are triggered by a foot step of the victim. A sensor that is able to detect minimum metal mines is a ground penetrating synthetic aperture radar (GPSAR). In previous work, an unmanned aerial vehicle (UAV)-based GPSAR was developed and tested for the detection of buried landmines. The real topography was neglected and the interface between air and soil was assumed as a horizontal plane surface. Since the dielectric properties of the soil reduce the propagation velocity of the electromagnetic wave, the interface between air and soil has to be known precisely for subsurface focusing. Neglecting the surface profile has a negative impact on the image quality and prevents the detection of buried objects. In this letter, a novel two-step procedure is used to overcome this issue. In time-division multiplex mode, the radar transmits two frequency-modulated ramps in two different frequency bands. The data of the upper frequency band are used to generate a digital elevation model (DEM) through interferometry. The data of the lower frequency band are used for GPSAR focusing on the basis of the DEM. It is demonstrated that the GPSAR image quality is improved on nonideal planar surfaces and the probability of detection is increased.

Improving Detection of a Portable NQR System for Humanitarian Demining Using Machine Learning

This article presents an approach to enhance the detection of buried landmines by applying machine learning (ML) to signals from a nuclear quadrupole resonance (NQR) system. This custom-made, low-cost, and portable NQR system has been developed for deployment in humanitarian demining, where strong radio frequency (RF) interference and a low signal-to-noise ratio (SNR) are important challenges for accurate detection. To tackle these problems, we have applied and tested various ML techniques to NQR signals acquired by our system in laboratory experiments with the explosive Research Department X (RDX). Results suggest that ML methods can indeed improve the detection accuracy of the NQR device, and this is confirmed further using data from field trials with our device. Importantly, the trained classifiers can be implemented with our device’s field-programmable gate array (FPGA) architecture and can run with little time penalty compared with simpler but less-efficient fast Fourier transform (FFT)-based energy detection methods.

Design of remote-controlled land mine detection troops safety robot

This paper describes a revolutionary intelligent system that detects explosive devices hidden in the ground by utilizing embedded system framework. The use of autonomous robots to discover explosives in surface-laid landmines is becoming more common. Hardware design and construction of remote-controlled Land Mine Detection Troops Safety Robot (LMDTSR) scan a specified area and determine the presence of landmines. The sandy and rugged terrain-like environment are the major search locations. When landmine is detected within the range of the robot, it produces a beeping sound at the location where the landmine is spotted. This robot is controlled autonomously by a PIC microcontroller. The robot is propelled by two internal DC motors. To assist motion, each motor will be connected to a wheel that is linked to another. When a mine is identified, the robot's electrical system commands the robot to come to a halt and mark the area where the mine is discovered. The robot is allowed to continue searching the area until another mine is discovered, at which point the procedure will repeat again. It goes over the techniques for using a metal detector to help the robot locate mines. The goal is to provide an overview of remote-controlled landmine detection procedures by using LMDTSR, which is capable of exploring, detecting, and labelling underground landmines.

Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR: A Novel Approach to Landmine Detection

Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this article, we propose a particle center AdaBoost (PCAD) method and achieve the multiparameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the AdaBoost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded two-dimensional image by PCAD, we can distinguish landmines from other targets.

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.

Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits.

Sense and sensibility: of synthetic biology and the redesign of bioreporter circuits.

A novel landmine detection system based on within and between subclasses dispersion information

ABSTRACT : Millions of anti-personnel landmines have been left in the ground during war conflicts in many countries, which causes many security, humanitarian and economic problems. It is therefore necessary to develop an automatic and efficient technique for landmines detection and localization, in order to clear the existing minefields. Ground-penetrating radar (GPR) is a geophysical method that is able to detect any buried object in the soil, but it suffers from high false alarm rate. Several landmine detection and localization systems have been developed, using GPR data and based on competitive classification methods, such as the One-class Support Vector Machine (OSVM), which handles the common problem of unbalanced data, in a proper manner. Nevertheless, in the landmine detection problem, we assume that the target class is composed of several subclasses related to metallic contents, which are not considered by related classification methods, including OSVM. For this reason, in this paper, we propose a subclass landmine detection and localization system based on a novel variation of the OSVM that takes into account the existence of subclasses in the landmine target class in order to jointly minimize the within and between subclass dispersion and to estimate the optimal decision function. Our proposed landmine detection and localization method has been evaluated and compared to other methods based on relevant one-class classifiers, on several real-world datasets extracted from the well-known MACADAM database. Experimental results have shown clearly that our proposed method is competitive with the existing relevant one-class classifiers, in landmine detection.

Honeybee-based biohybrid system for landmine detection

Legacy landmines in post-conflict areas are a non-discriminatory lethal hazard and can still be triggered decades after the conflict has ended. Efforts to detect these explosive devices are expensive, time-consuming, and dangerous to humans and animals involved. While methods such as metal detectors and sniffer dogs have successfully been used in humanitarian demining, more tools are required for both site surveying and accurate mine detection. Honeybees have emerged in recent years as efficient bioaccumulation and biomonitoring animals. The system reported here uses two complementary landmine detection methods: passive sampling and active search. Passive sampling aims to confirm the presence of explosive materials in a mine-suspected area by the analysis of explosive material brought back to the colony on honeybee bodies returning from foraging trips. Analysis is performed by light-emitting chemical sensors detecting explosives thermally desorbed from a preconcentrator strip. The active search is intended to be able to pinpoint the place where individual landmines are most likely to be present. Used together, both methods are anticipated to be useful in an end-to-end process for area surveying, suspected hazardous area reduction, and post-clearing internal and external quality control in humanitarian demining.

Use of laser speckle shearing interferometric vibration measurement system for acoustic-to-seismic landmine detection

To study the application of laser shearing speckle interferometric vibration measurement technology in acoustic resonance landmine detection, we present an experimental and theoretical study of the method for laser shearing speckle interferometric landmine detection based on time-average. The relationship between the out-of-plane displacement gradient generated by the sound wave on the ground and the phase change of the shearing interference laser is analyzed. A high-power loudspeaker is used as the source of sound wave excitation, and the laser shearing speckle interferometric vibration measurement system is used in landmine detection experiments to investigate the changes of laser shearing speckle interference fringes under different experimental conditions such as sound wave intensity, detection target, buried depth, and soil environment. The study reveals that it is feasible and effective to use laser shearing speckle interferometry to detect landmines, and it provides a theoretical and methodological basis for realizing the rapid scanning of acoustic-optic landmine detection.

Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks.

The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.

Automated microwave monitoring of hidden objects for strategic and security applications

An automated microwave technique for the monitoring of hidden objects for strategic applications has been presented. The proposed approach is quite novel as it implements and integrates the time-domain method in free-space with the indigenously developed automated scanning setup to obtain the dielectric profile of hidden objects in standard sand or behind the concrete structure. The existing imaging methods for concealed media cannot reveal detailed information of objects with restrictions to access the other side of the test media. The overall approach is implemented by measuring the S-parameters of the test region containing the hidden objects in the wide frequency range of 2-10 GHz using the horn-lens antenna and the network analyzer. The S-parameters are transformed into the equivalent time-domain using the standard IFFT algorithm to retrieve the test region's 2D microwave image and the approximate size of concealed objects, which helps in situations such as through-the-wall imaging, and landmine detection.

IOT based Land Mine Detection Robot

Such equipment is usually automatically blasted through pressure when a target steps on it or drives on it. Demining or mine clearance is the process of removing land mines from an area, while minesweeping describes the task of locating mines. Landmines are generally easy to lay and difficult and dangerous. They are harmful due to their unknown positions and are often difficult to detect. Although dimming has been given the highest priority, currently my clearing operation is a labor-intensive, slow, very dangerous, expensive and low technology operation. The current rate of human quarry is about 100 thousand per year. The idea is to build and develop an automated robot capable of detecting buried landmines and taking them from their locations, enabling the operator to control the robot from afar. The buried quarry is detected using metal detectors as most land mines have metal components. The robot will travel in a straight line route. The system allows the operator to stay at a safe distance by enabling the robot to safely control. Wireless control is for the safety of the operator, designed robots must be able to operate remotely, and is equipped with wireless data transmission capabilities.

Simulation Study on Landmines Detection by Pulsed Fast Thermal Neutron Analysis

Simulation Study on Landmines Detection by Pulsed Fast Thermal Neutron Analysis

Nuclear quadrupole resonance response detection using deep neural networks

Nuclear quadrupole resonance is a solid-state spectroscopy technique used for analyzing substances with quadrupolar nuclei. The technique is highly specific and used in multiple security applications, such as landmine detection and luggage or personnel screening for prohibited substances. However, the performance of existing detection systems needs improvement, especially taking into account the high risk associated with explosives, drugs and narcotics. This paper proposes the application of deep learning techniques to improve the detection’s performance. A new feature set is proposed for signal classification and multiple high-performance solutions are selected, adapted and implemented for quadrupole resonance detection. A comparative analysis is performed on a real data set composed of 20,000 acquisitions and AlexNet is shown to outperform the other algorithms. It ensures a detection accuracy of 99.9%, which is remarkable from an operational standpoint and surpasses the performance of existing signal processing and analysis solutions. The solution is deployed on a commercial computer integrated into the spectrometer and has an analysis time of approx. 1 s, which does not significantly affect the overall scan time.

Numerical Design and Experimental Validation of a Plastic 3D-Printed Waveguide Antenna for Shallow Object Microwave Imaging

Microwave imaging of shallow buried objects has been demonstrated with holographic radar for landmine detection, civil engineering and cultural heritage. A key component of this system is the antenna based on a truncated cylindrical waveguide with two feeds. This paper investigates for the first time a manufacturing technology based on the 3D printing of a volumetric cylindrical plastic antenna. The investigation of this manufacturing technology was motivated by the reduction in the antenna size and customization of the electromagnetic characteristics to the radio frequency electronics mounted on the robotic scanning system. The antenna that was designed using a simulator and filled with polylactic acid plastic material (relative dielectric permittivity Ɛr = 2.5) is compared to the metal antenna, both operating at around 2 GHz. The goal was to replicate the characteristics of the void core antenna to be able to provide the same quality/information of the microwave images of shallow buried objects. Finally, we compared the scan results of dielectric and metal targets both in the air and in natural soil. From the observation of some of the characteristics of the images, such as dynamics, morphology of the target, signal-to-noise ratio, and operating distance, we demonstrate that 3D printing for volumetric cylindrical waveguide antenna could be used to obtain compact and easily adaptable antennas for different applications in remote sensing.

Application of a Drone Magnetometer System to Military Mine Detection in the Demilitarized Zone.

We propose a magnetometer system fitted on an unmanned aerial vehicle (UAV, or drone) and a data-processing method for detecting metal antipersonnel landmines (M16) in the demilitarized zone (DMZ) in Korea, which is an undeveloped natural environment. The performance of the laser altimeter was improved so that the drone could fly at a low and stable altitude, even in a natural environment with dust and bushes, and a magnetometer was installed on a pendulum to minimize the effects of magnetic noise and vibration from the drone. At a flight altitude of 1 m, the criterion for M16 is 5 nT. Simple low-pass filtering eliminates magnetic swing noise due to pendulum motion, and the moving average method eliminates changes related to the heading of the magnetometer. Magnetic exploration was conducted in an actual mine-removal area near the DMZ in Korea, with nine magnetic anomalies of more than 5 nT detected and a variety of metallic substances found within a 1-m radius of each detection site. The proposed UAV-based landmine detection system is expected to reduce risk to detection personnel and shorten the landmine-detection period by providing accurate scientific information about the detection area prior to military landmine-detection efforts.

Wireless Operated War Field Land Rover That Alerts On Sensing Planted Land Mines

Land mine detection is most crucial during ware fare to deploy armed vehicle drive in the enemy territory. These armed vehicle or Main battle tanks are used to follow the path of pilot tank operated manually to avoid damage/distraction of the battle tank and defence causalities of defence crews. In addition post ware fare the mines planted during war can be detected and diffused by deploying a mine detection robot, which can save civilian life to avoid human causalities. This research work proposed to have a proto type model of a land-mine detection robot (LDR), which can be operated remotely using Wi-fi. technology. The safety of humans was addressed and designed robot with special range sensors employed to avoid obstacles. Fabrication of this project prototype was done using lightweight temperature resistant metal. A Global Positioning System (GPS) sensor is employed, which identifies and broadcasts the present location of the robot. Path planning, obstacle detection and avoidance algorithms were used to control accurately and to navigation of the proposed path by avoiding obstacles. Arduino microcontroller is employed in this robot. The robot system is embedded with metal detector capable of sensing the landmine and buzzer from producing a warning alarm to the nearby personnel in that area. The locomotion of the robot is carried out by the DC motor. The robot is interfaced with the PC by deploying a ZigBee device. In this paper, a Metal Detection System with GSM technology has been presented. The system senses or detects a metallic object and sends an alert message in form of an SMS text to an embedded security GSM number for further actions

A cost-effective GPS-aided autonomous guided vehicle for global path planning

This paper presents a robotic platform of a cost-effective GPS-aided autonomous guided vehicle (AGV) for global path planning. The platform is made of a mechanical radio controlled (RC) rover and an Arduino Uno microcontroller. An installed magnetic digital compass helps determine the right direction of the RC rover by continuously synchronising the heading and bearing of the vehicle. To ensure effective monitoring of the vehicle’s position as well as track the corresponding path, an LCD keypad shield was, further, used. The contribution of the work is that the designed GPS-aided AGV can successfully navigate its way towards a destination point in an obstacle-free outdoor environment by solely relying on its calculation of the shortest path and utilising the corresponding GPS data. This result is achieved with a minimum error possible that lies within a circle of one meter radius around the given destination, allowing the devised GPS-aided AGV to be used in a variety of applications such as landmine detection and removal.

Simulation of PGNAA Landmine Detection Method by MOCA Program

Simulation of PGNAA Landmine Detection Method by MOCA Program