Scene image recognition with knowledge transfer for drone navigation

被引：0

作者：

DU Hao ^{[1
,2
]}

WANG Wei ^{[2
,3
]}

WANG Xuerao ^{[1
]}

ZUO Jingqiu ^{[2
]}

WANG Yuanda ^{[1
]}

机构：

[1] School of Automation, Southeast University

[2] Autonomous Control Robot Laboratory, Jiangsu Zhongke Institute of Applied Research on Intelligent Science and Technology

[3] Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology

来源：

JournalofSystemsEngineeringandElectronics | 2023年 / 34卷 / 05期

关键词：

D O I：

暂无

中图分类号：

TP391.41 []; V279 [无人驾驶飞机]; V249.3 [导航];

学科分类号：

080203 ; 1111 ; 081105 ;

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 problem in the classifier, and we use global navigation satellite systems(GNSS) to solve it in the prediction stage. Experiment results show our method, with a pre-trained model and fine tuning, can achieve 91.319 6% top-1 accuracy on Scenes21dataset, paving the way for drones to learn to understand the scenes around them autonomously.

引用

页码：1309 / 1318

页数：10

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