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Paper Abstract and Keywords
Presentation 2019-03-05 11:50
An optimization method of wireless communication bandwidth in Drone object recognition using edge computing
PengFei Sun, Akihiro Nakao (Tokyo Univ.) NS2018-250
Abstract (in Japanese) (See Japanese page) 
(in English) Real-time object detection is considered challenging in many UAV applications, such as detection, surveillance, search and rescue, etc. There have been many real-time object recognition models based on deep learning, which however require high computational resources. Generally, the cloud and edge servers are adopted to perform computing tasks based on images captured by the drone cameras.
The most challenging problem with this approach is that, high-definition images are captured and transmitted to the cloud server, the need for network bandwidth is extremely high for real-time object detection, the service by multiple drones is not to scaled out. We suppose the object-recognition is performed via deep neutral network, which is usually applied to the object features abstracted from images as intermediary data. For this reason, our proposed method splits a deep neural network model and executes a part of computation to obtain intermediary data, which is supposed to be smaller than the size of original images. Our approach preserves the accuracy of object recognition at a high level while reducing the volume of transferred wireless data. Moreover, we use additional computational resources for extra layers of the neural network, yet lightweight enough to be executed on a drone, to compress the intermediary data so as to reduce the wireless network bandwidth. Our data compression is optimized for machine learning instead of human eyes so that it reduces the data transmission by as much as 64% compared with the general approach, while the accuracy of object detection is reduced only by 3% .
Keyword (in Japanese) (See Japanese page) 
(in English) edge computing / AI / Object detection / drone / 5G / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 465, NS2018-250, pp. 337-342, March 2019.
Paper # NS2018-250 
Date of Issue 2019-02-25 (NS) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2018-250

Conference Information
Committee IN NS  
Conference Date 2019-03-04 - 2019-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2019-03-IN-NS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An optimization method of wireless communication bandwidth in Drone object recognition using edge computing 
Sub Title (in English)  
Keyword(1) edge computing  
Keyword(2) AI  
Keyword(3) Object detection  
Keyword(4) drone  
Keyword(5) 5G  
1st Author's Name PengFei Sun  
1st Author's Affiliation The University of Tokyo (Tokyo Univ.)
2nd Author's Name Akihiro Nakao  
2nd Author's Affiliation The University of Tokyo (Tokyo Univ.)
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Speaker Author-1 
Date Time 2019-03-05 11:50:00 
Presentation Time 20 minutes 
Registration for NS 
Paper # NS2018-250 
Volume (vol) vol.118 
Number (no) no.465 
Page pp.337-342 
Date of Issue 2019-02-25 (NS) 

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