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Paper Abstract and Keywords
Presentation 2018-10-18 13:50
[Poster Presentation] An optimization method of wireless communication bandwidth in drone object recognition using edge computing
PengFei Sun, Akihiro Nakao (The University Of Tokyo) NS2018-114
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 and need to be run with high-end Graphics Processing Units (GPUs). 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, due to a large amount of data transferred between the drone and cloud server, the need for network bandwidth is extremely high for real-time object detection. Although 5G supports high bandwidth, if we would like to scale the number of UEs, the less bandwidth the service uses, the more scalable the service becomes. 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 less than the final output data. Our approach preserves the accuracy of object recognition at a high level while reducing the wireless data transfer bandwidth. Moreover, we use additional computational resources for extra layers of the neural network, yet negligible enough to be executed on a drone, to compress the intermediary data so as to reduce the wireless network bandwidth. The usual data compression is for the sake of human eyes recognition, but our data compression is optimized for machine learning and executed by additional neural network layers. Using Yolo V2[1] , we evaluate our proposed method to find the detection accuracy of the proposed method reduces the data transmission by as much as 84% compared with the general approach, while the accuracy of object detection is reduced only by 3%.
Keyword (in Japanese) (See Japanese page) 
(in English) 5G / edge computing / AI / Object detection / drone / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 250, NS2018-114, pp. 45-48, Oct. 2018.
Paper # NS2018-114 
Date of Issue 2018-10-11 (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 NS2018-114

Conference Information
Committee NS  
Conference Date 2018-10-18 - 2018-10-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Kyoiku Bunka Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network architecture (Overlay, P2P, Ubiquitous network, Scale-free network, Active network, NGN, New Generation Network), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. 
Paper Information
Registration To NS 
Conference Code 2018-10-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) 5G  
Keyword(2) edge computing  
Keyword(3) AI  
Keyword(4) Object detection  
Keyword(5) drone  
1st Author's Name PengFei Sun  
1st Author's Affiliation The University Of Tokyo (The University Of Tokyo)
2nd Author's Name Akihiro Nakao  
2nd Author's Affiliation The University Of Tokyo (The University Of Tokyo)
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Speaker Author-1 
Date Time 2018-10-18 13:50:00 
Presentation Time 90 minutes 
Registration for NS 
Paper # NS2018-114 
Volume (vol) vol.118 
Number (no) no.250 
Page pp.45-48 
Date of Issue 2018-10-11 (NS) 

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