摘要
无人机的快速发展与应用在给社会带来便利的同时,也对公共安全、个人隐私、军事安全等构成了严重威胁。快速准确地发现未知无人机变得越来越重要。在无人机检测技术中,基于机器视觉的方法具有成本低廉和配置简便的优点。针对低空快速移动的无人机,提出一种基于优化YOLOv3的无人机检测识别方法。利用残差网络及多尺度融合的方式对原始的YOLO网络结构进行优化,提出O-YOLOv3网络,利用真实拍摄的无人机数据集进行训练和测试。实验结果表明,所提方法的平均准确度优于原始方法,检测速度满足实时性要求。
The rapid development and application of unmanned aerial vehicles(UAVs)not only bring convenience to the society,but also pose serious threats to public security,personal privacy,and military security.Therefore,rapid and accurate detection of unknown UAV becomes increasingly important.In addition,in UAV detection technology,the method based on machine vision has the advantages of low cost and simple configuration.This paper proposes an optimized YOLOv3(You Only Look Once version3)based detection and recognition method for low altitude and fast moving UAV.The residual network and multi-scale fusion are used to optimize the network structure of the original YOLO,and the O-YOLOv3network is proposed.The training and testing are carried out using the real filmed UAV dataset.The experimental results show that the average precision of the optimized method is better than that of the original method,and the detection speed meets the real-time requirement.
作者
马旗
朱斌
张宏伟
张杨
姜雨辰
Ma Qi;Zhu Bin;Zhang Hongwei;Zhang Yang;Jiang Yuchen(State Key Laboratory of Pulsed Power Laser Technology,College of Electronic Engineering,National University of Defense Technology,Hefei,Anhui 230037,China;National University of Defense Technology,Hefei,Anhui 230037,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第20期271-278,共8页
Laser & Optoelectronics Progress
关键词
图像处理
低空无人机
目标检测
残差网络
多尺度融合
image processing
low-altitude unmanned aerial vehicles
target detection
residual network
multiscale fusion
