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Paper Abstract and Keywords
Presentation 2023-02-22 11:30
Implementation of Hierarchical Object Detection Method for Super High-Definition Image Sensing
Makoto Sugaya, Yusei Horikawa, Renpei Yoshida, Tetsuya Matsumura (Nihon Univ.) ITS2022-65 IE2022-82
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a hierarchical object detection method for a 4K super-high definition. This method is a three-layer detector configuration consisting of wide-area detection, local-area detection, and combined processing. For 4K support, we have proposed and implemented a reconstruction image generation algorithm for optimization of the image size for inference and local-area detection. For the evaluation, we upsampled to 4K size based on the aerial image taken by the drone and used it as a dataset. As a result of the evaluation, when comparing the hierarchical object detection before and after supporting 4K images, the number of detected objects increased by 8,051, and the mAP improved by 9.03 points. In addition, comparing the conventional method and the new reconstructed image generation method for hierarchical object detection for 4K images, the value of mAP did not increase or decrease, and the detection speed was reduced by about 6%. Through our evaluation, the superiority of this method was verified.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Object Detection / Super-High Resolution / YOLO / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 385, IE2022-82, pp. 130-135, Feb. 2023.
Paper # IE2022-82 
Date of Issue 2023-02-14 (ITS, IE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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)
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Conference Information
Committee IE ITS ITE-MMS ITE-ME ITE-AIT  
Conference Date 2023-02-21 - 2023-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To IE 
Conference Code 2023-02-IE-ITS-MMS-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Implementation of Hierarchical Object Detection Method for Super High-Definition Image Sensing 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Object Detection  
Keyword(3) Super-High Resolution  
Keyword(4) YOLO  
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1st Author's Name Makoto Sugaya  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Yusei Horikawa  
2nd Author's Affiliation Nihon University (Nihon Univ.)
3rd Author's Name Renpei Yoshida  
3rd Author's Affiliation Nihon University (Nihon Univ.)
4th Author's Name Tetsuya Matsumura  
4th Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2023-02-22 11:30:00 
Presentation Time 15 minutes 
Registration for IE 
Paper # ITS2022-65, IE2022-82 
Volume (vol) vol.122 
Number (no) no.384(ITS), no.385(IE) 
Page pp.130-135 
#Pages 6 
Date of Issue 2023-02-14 (ITS, IE) 

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