舰船电子工程2019，Vol.39Issue(2)：18-22,47,6.DOI:10.3969/j.issn.1672-9730.2019.02.007
基于深度卷积神经网络的无人机识别方法研究
Research on UAV Recognition Method Based on DCNN
摘要
Abstract
Deep Convolutional Neural Network(DCNN)can extract the image features automatically. An Unmanned Aerial Ve?hicle(UAV)recognition method based on DCNN is proposed to solve the problems of low detection accuracy,difficulty in accurate?ly extracting the UAV picture from the video,and classifying of different UAVs. Firstly,the UVA targets are detected from the video by using Single Shot MultiBox Detector(SSD)algorithm. Then an efficient model of recognition is obtained through training a learn?ing network based on Visual Geometry Group(VGG)16. The UVA detected images are put into VGG16 model for feature extrac?tion. Finally,the classification of different UAVs is accomplished. Back Propagation(BP)algorithm is introduced to improve the ro?bustness of the method in network model optimizing phase. Experiments show that the method has higher recognition rate and better engineering application.关键词
深度卷积神经网络/无人机分类/无人机识别/特征提取/识别模型Key words
deep convolutional neural network/UAV classification/UAV recognition/feature extraction/recognition model分类
信息技术与安全科学引用本文复制引用
刘佳铭..基于深度卷积神经网络的无人机识别方法研究[J].舰船电子工程,2019,39(2):18-22,47,6.
