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| Presentation | 2019-03-18 15:00 Reali-time Object Detection based on SSD Yuya Yamashige, Masaki Aono (TUT)  BioX2018-60 PRMU2018-164 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, attention has been paid to developing object detection methods from images, based on deep learning. In particular, toward self-driving cars, it is essential to make the detection as accurately as possible, as quickly as possible, as economically as possible. Here, we focus on SSD: Single Shot MultiBox Detector (SSD300) and attempt to improve it with less GPU memory. We propose a new method called “FPSSD7” (Feature Pyramid SSD7), which has less convolutional layers, keeping semantic information. For this purpose, we develop a method to interpolate semantic information between lower layers with high resolution feature maps and upper layer with low resolution features maps. We conducted comparative experiments with the conventional methods using Udacity Annotated Driving Dataset. As the result, we demonstrate that our proposed method outperform the conventional methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Object Detection / Deep Learning / SSD: Single Shot MultiBox Detector / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 513, PRMU2018-164, pp. 181-186, March 2019. | |
| Paper # | PRMU2018-164 | |
| Date of Issue | 2019-03-10 (BioX, PRMU) | |
| ISSN | Print edition: ISSN 0913-5685 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) | |
| Download PDF | BioX2018-60 PRMU2018-164 |
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| Conference Information | |
| Committee | PRMU BioX |
| Conference Date | 2019-03-17 - 2019-03-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2019-03-PRMU-BioX |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Reali-time Object Detection based on SSD |
| Sub Title (in English) | |
| Keyword(1) | Object Detection |
| Keyword(2) | Deep Learning |
| Keyword(3) | SSD: Single Shot MultiBox Detector |
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| 1st Author's Name | Yuya Yamashige |
| 1st Author's Affiliation | Toyohashi University of Technology (TUT) |
| 2nd Author's Name | Masaki Aono |
| 2nd Author's Affiliation | Toyohashi University of Technology (TUT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-18 15:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | BioX2018-60, PRMU2018-164 |
| Volume (vol) | vol.118 |
| Number (no) | no.512(BioX), no.513(PRMU) |
| Page | pp.181-186 |
| #Pages | 6 |
| Date of Issue | 2019-03-10 (BioX, PRMU) |
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