Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

Airplane Recognition and Location On the Airport Based On Computer Vision

Authors
JianFeng Mu, YanYang Wang
Corresponding Author
JianFeng Mu
Available Online September 2016.
DOI
10.2991/icence-16.2016.80How to use a DOI?
Keywords
Airplane; Computer Vision; Recognition; Location; Security;
Abstract

In order to improve safety for airliner ground movement at the airport, a positioning method which can identify airliner and find the location of the airliner when the airliner taxiing at the airport based on computer vision is presented. Deep learning method is applied to recognize an airliner from complicated background of an image. After that, image segmentation based on graph is used to acquire the outline of the airliner. Then, after obtaining the nose undercarriage tyre, the location of airliner is obtained by means of coordinate transformation. The simulation results illustrate the validity of the proposed method.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
Series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
10.2991/icence-16.2016.80
ISSN
2352-538X
DOI
10.2991/icence-16.2016.80How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - JianFeng Mu
AU  - YanYang Wang
PY  - 2016/09
DA  - 2016/09
TI  - Airplane Recognition and Location On the Airport Based On Computer Vision
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 416
EP  - 422
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.80
DO  - 10.2991/icence-16.2016.80
ID  - Mu2016/09
ER  -