基于深度学习的无人机入侵检测研究  

Research on UAV Instrusion Dectection Based on Deep Learning

作　　者：张根源 林智伟[1] 舒立鹏[1] 王美懿 ZHANG Genyuan;LIN Zhiwei;SHU Lipeng;WANG Meiyi(Northwest Institute of Mechanical&Electrical Engineering,Xianyang 712099,Shaanxi,China;Law Press of China,Beijing 100073)

机构地区：[1]西北机电工程研究所,陕西咸阳712099 [2]法律出版社有限公司,北京100073

出　　处：《火炮发射与控制学报》2024年第4期21-25,34,共6页Journal of Gun Launch & Control

摘　　要：针对现有探测系统探测低空、慢速、小型、低RCS、弱红外辐射特性的无人机类目标的能力不足,无法有效地通过图像侦测到无人机入侵的问题,采用基于深度学习的方法来对无人机图像进行识别和检测,提出了以YOLOv5目标检测模型为基础同时结合ResNet50分类模型的无人机入侵检测方法。经过实验对比结果表明:该方法识别Drone-detection-dataset数据集中4种空中目标的准确率、召回率和均值平均精度值均高于原始YOLOv5模型;且相比于Faster-RCNN和SSD检测模型,YOLOv5+ResNet50检测和识别无人机图像的效果更优,特别是针对于远距离的无人机图像。因此YOLOv5+ResNet50可以有效地对入侵无人机进行检测和预警。In view of the insufficient capability of existing detection system to detect UAV targets with low altitude,slow speed,low RCS and weak infrared radiation,and difficulties of detecting drones effectively through images,the method based on YOLOv5 and ResNet50 models was proposed,and the deep learning method was used to detect and identify UAV images.Comparing with original YOLOv5model,accuracy,recall and mAP of identifying aerial targets in Drone-detection-dataset are higher based on the proposed model.Besides,comparing with Faster-RCNN and SSD,YOLOv5+ResNet50 could reach better results of detecting UAV images,especially for distant UAV targets.As a result,YOLOv5+ResNet50 can be used to detect UAV efficiently.

关 键 词：无人机检测 深度学习 空中目标 YOLOv5 ResNet50 

分 类 号：TJ012.3[兵器科学与技术—兵器发射理论与技术] TP301.6[自动化与计算机技术—计算机系统结构]