Scene image recognition with knowledge transfer for drone navigation  

作　　者：DU Hao WANG Wei WANG Xuerao ZUO Jingqiu WANG Yuanda 

机构地区：[1]School of Automation,Southeast University,Nanjing 210096,China [2]Autonomous Control Robot Laboratory,Jiangsu Zhongke Institute of Applied Research on Intelligent Science and Technology,Changzhou 213164,China [3]Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China

出　　处：《Journal of Systems Engineering and Electronics》2023年第5期1309-1318,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(62103104);the Natural Science Foundation of Jiangsu Province(BK20210215);the China Postdoctoral Science Foundation(2021M690615).

摘　　要：In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

关 键 词：scene recognition convolutional neural network knowledge transfer global navigation satellite systems(GNSS)-aided 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] V279[自动化与计算机技术—计算机科学与技术] V249.3[航空宇航科学与技术—飞行器设计]