Automatic Detection of Wrecked Airplanes from UAV Images

EMITTER International Journal of Engineering Technology

View Publication Info
 
 
Field Value
 
Title Automatic Detection of Wrecked Airplanes from UAV Images
 
Creator Risnumawan, Anhar
Perdana, Muhammad Ilham
Alif Habib Hidayatulloh
A. Khoirul Rizal
Indra Adji Sulistijono
Achmad Basuki
Rokhmat Febrianto
 
Subject Wrecked airplanes detection
UAV image
deep learning method
real-time detector
extra layers
 
Description Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.
 
Publisher Politeknik Elektronika Negeri Surabaya (PENS)
 
Date 2019-12-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://emitter.pens.ac.id/index.php/emitter/article/view/424
10.24003/emitter.v7i2.424
 
Source EMITTER International Journal of Engineering Technology; Vol 7 No 2 (2019); 570-585
2443-1168
2355-391X
10.24003/emitter.v7i2
 
Language eng
 
Relation http://emitter.pens.ac.id/index.php/emitter/article/view/424/177
 
Rights Copyright (c) 2019 EMITTER International Journal of Engineering Technology
http://creativecommons.org/licenses/by-nc-sa/4.0
 

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