Paper Abstract and Keywords |
Presentation |
2019-12-19 14:00
Efficient Object Detection for Railway Crossing Combining Image Classification and Object Detection Kaisei Shimura, Yoichi Tomioka, Qiangfu Zhao (UoA) PRMU2019-52 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this research, we aim to develop a system to support drivers to prevent accidents when driving an mobility scooter. As the first step of driving support, in this paper, we proposes a method for efficiently detecting an object which exists close to a railway crossing normally. In this method, an image classification is performed before object detection to detect railway crossing scene. By applying object detection only to a crossing scene, the workload and false detection for object detection are reduced.In the experiments, the precision and F-score for each class was improved by at most 0.224 and 0.234, respectively. Moreover, we achieved 1.7 to 2.0 times faster execution for scenes in which a railway crossing does not exist on desktop PC, Raspberry Pi 3 model B equipped with Neural Compute Stick 2. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
railway crossing / deep learning / image classification / object detection / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 347, PRMU2019-52, pp. 35-39, Dec. 2019. |
Paper # |
PRMU2019-52 |
Date of Issue |
2019-12-12 (PRMU) |
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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PRMU2019-52 |
Conference Information |
Committee |
PRMU |
Conference Date |
2019-12-19 - 2019-12-20 |
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(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2019-12-PRMU |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Efficient Object Detection for Railway Crossing Combining Image Classification and Object Detection |
Sub Title (in English) |
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Keyword(1) |
railway crossing |
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deep learning |
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image classification |
Keyword(4) |
object detection |
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1st Author's Name |
Kaisei Shimura |
1st Author's Affiliation |
University of Aizu (UoA) |
2nd Author's Name |
Yoichi Tomioka |
2nd Author's Affiliation |
University of Aizu (UoA) |
3rd Author's Name |
Qiangfu Zhao |
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University of Aizu (UoA) |
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Speaker |
Author-1 |
Date Time |
2019-12-19 14:00:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2019-52 |
Volume (vol) |
vol.119 |
Number (no) |
no.347 |
Page |
pp.35-39 |
#Pages |
5 |
Date of Issue |
2019-12-12 (PRMU) |
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