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

作　　者：Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour 

机构地区：[1]Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,Saudi Arabia [2]Department of Computer Science and Engineering,Thapar Institute of Engineering and Technology,Deemed to be University,Patiala,India

出　　处：《Computer Modeling in Engineering & Sciences》2024年第5期2117-2137,共21页工程与科学中的计算机建模（英文）

基　　金：funded by Institutional Fund Projects under Grant No(IFPNC-001-611-2020).

摘　　要：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%.

关 键 词：CNN deep learning landmine detection MAGNETOMETER mean average precision UAV 

分 类 号：TJ5[兵器科学与技术—军事化学与烟火技术]