A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human lives.The detonation of these landmines results in thousands of casualties reported worldwide annually.Therefore,there is a pressing need to employ diverse landmine detection techniques for their removal.One effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic field.It can generate a contour plot or heat map that visually represents the magnetic field strength.Despite the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith risks.Edge computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine detection.By processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field data.It enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the process.Furthermore,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during transmission.This paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and localization.We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces.By simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry images.The trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

3D Characteristic Diagram of Acoustically Induced Surface Vibration with Different Landmines Buried

The 3D characteristic diagram of acoustically induced surface vibration was employed to study the influence of different buried landmines on the acoustic detection signal. By using the vehicular experimental system for acoustic landmine detection and the method of scanning detection, the 3D characteristic diagrams of surface vibration were measured when different objects were buried underground, including big plastic landmine, small plastic landmine, big metal landmine and bricks. The results show that, under the given conditions, the surface vibration amplitudes of big plastic landmine, big metal landmine, small plastic landmine and bricks decrease in turn. The 3D characteristic diagrams of surface vibration can be used to further identify the locations of buried landmines.

A NOVEL SVM FOR GROUND PENETRATING SYNTHETIC APERTURE RADAR LANDMINE DETECTION

The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

Evaluating some factors that affect feasility of using ground penetrating radar for landmine detection

Ground penetrating radar （GPR） is one of the promising technologies that can be used to detect landmines. Many factors may affect the ability of GPR to detect landmines. Among those factors are： 1） the type of landmine material （metallic or plastic）, 2） conditions of the host soil （soil texture and soil moisture）, and 3） the radar frequency utilized. The impact of these factors on the ability of GPR to detect landmines is investigated by studying their effect on the dielectric permittivity contrast between the landmine and the host soil, as well as on the attenuation of the radar waves. The impact of each factor was theoretically reviewed and modeled using the Matlab and Mathcad software packages. Results of the computer modeling were correlated with GPR data acquired for metallic and plastic landmine types. It was found that the ability of GPR to detect landmines depends to a great extent on the landmine type, water content of the host soil, utilized radar frequency, and soil texture. The landmines are much easier to detect than plastic landmines for any soil conditions and any radar frequency. Increasing the soil＇s moisture content, regardless of soil texture, eases the detection of the plastic landmine and worsens the detection of the metallic mines. Increasing the percentage of clay in the soil causes the same effect as the moisture content. However, higher radar frequency delivers better results for landmine detection as long as the percentage of clay and the moisture content in the soil remains low. The results of this study are expected to help in selecting optimum radar antennae and data acquisition parameters depending on the landmine type and environmental conditions.

Method for Detecting Multi-Modal Vibration Characteristics of Landmines

The acoustic vibration characteristics of landmines are investigated by means of modal analysis. According to the mechanical structure of landmines, a certain number of points are marked on the landmine shell to analyze its multi-modal vibration characteristics, based on laser self-mixing interferometer and taking 69 plastic landmine as an example, the vibration detection experiment system is built to show the results of analytical method of multi-modal testing. The first and second order natural frequencies of the bricks are 38 HZ and 106 HZ, 112 HZ and 232 HZ for plastic landmines, and 74 HZ and 290 HZ for metal landmines. The first and second order natural frequencies of the bricks are far smaller than those of plastic landmines and metal landmines. This indicates that landmines show multi-modal vibration characteristics under external excitation, which are significantly different from those of bricks. The findings can be used for further research on acoustic landmines detection technology.

Numerical simulation of the coupling of ultra-wide band electromagnetic pulse into landmine by aperture

The modern landmine＇s electronic fuse is susceptible to strong interference or can even be damaged by the ultra-wide band electromagnetic pulse（UWB-EMP）. The finite-difference time-domain（FDTD） method in lossy media with cylindrical coordinates is used to study the interactions of the UWB-EMP with the landmine. First, the coupling of UWB-EMP into the landmine shielding shell through an aperture is numerically simulated. Second, the coupled electromagnetic field of mine shells made of different shielding materials and with apertures of different sizes is plotted. Third, the aperture coupling laws of UWB-EMP into shells are analyzed and categorized. Such an algorithm is capable of effectively preventing ladder similar errors, and consequently improving the calculation precision, and in addition to adopting the message passing interface（MPI） parallel method to divide the total calculating range into more sub-ranges, the overall calculating efficiency is greatly increased. These calculations are surely a constructive reference for modern landmine design against electromagnetic damage.

Mental health disorders in child and adolescent survivors of post-war landmine explosions

Background: To describe the mental health status of 78 child and adolescent survivors of post-war landmine explosions.Methods: Child and adolescent survivors of landmine explosions who were younger than 18 years old at the time of the study were identified and enrolled in this study. The mental health status of the participants was assessed by general health assessment and psychiatric examinations. Psychiatric assessment and diagnosis were undertaken using the Diagnostic and Statistical Manual for mental disorders(DSM-IV) criteria. A psychiatrist visited and interviewed each survivor and identified psychiatric disorders.Results: Seventy-eight child and adolescent survivors with a mean age of 16.11±2 years old were identified and agreed to participate in the study. The mean age of the victims at the time of injury was 8.2±3.12 years old(range 2–15). Thirty-seven(47.4%) of the adolescent survivors suffered from at least one psychiatric disorder. Twenty-nine survivors(37.1%) were newly diagnosed and needed to start medication and psychiatric treatment. The most common findings were anxiety disorders(34.6%), including posttraumatic stress disorder(PTSD) in 20(25.6%), and generalized anxiety disorder(GAD) in 7(9%) subjects. Mild-Moderate depression was found in 5(6.4%) subjects. No personality disorders were observed, and two patients suffered from mental retardation. The study results revealed a significant association between age of casualty, duration of injury and limb amputation, and types of psychological disorders.Conclusion: Child and adolescent survivors of landmine explosions had a high prevalence of psychiatric disorders.

Investigation the Effects of Neutron Source Number and Arrangement in Landmines Detection by Thermal Neutron Capture Gamma Ray Analysis

Thermal neutron capture can be used as a successful technique for detection of non-metallic landmines via the detection of their constituent like nitrogen. Recently, it has been shown that the detection of 10.829 MeV photons from the 14N(n,γ)15N reaction can be used for finding the landmines. In this method a high-energy neutron source like 241Am-Be inside water as a moderator is used to have thermal neutron. In this paper we have investigated the effects of the number of neutron sources and their orientation on the gamma ray spectrum by using MCNP4C code. The best case for number of sources and their positions and orientations have been achieved corresponding to maximum flux of 10.829 MeV photons.

Rats Train As Landmine Detectors

鼠,也有“改邪归正”之日！比利时的研究人员正在训练老鼠当地雷侦察员。收效甚奇:Trained rats may be the best and cheapest form of landmine(地雷)detector. 不过,老鼠心甘情愿地去发现地雷,并非被灌输了什么“为民除害”的思想,而是为了食物,为了生存: Once the minefield has been mapped,the rats sniff out a landmine and sitbeside it waiting to be rewarded with food.

Experimental measurement of acoustically induced surface vibration with different soil conditions

Experimental measurement is performed to investigate the acoustically induced surface vibration with different soil conditions. Using the method of scanning detection and analyzing the three-dimensional （3D） char- acteristic diagram of surface vibration, the influence of soil properties, such as porosity and humidity, upon the signal of acoustically induced surface vibration is measured. The experimental results show that the surface vibration redu- ces with the decrease of soil porosity and reduces a little with the increase of soil humidity; and with a big plastic landmine buried, the surface vibration enhances signifi- cantly. It indicates that the signal of acoustically induced surface vibration mainly depends on soil porosity and mechanical effect of buried objects.