Paper Abstract and Keywords |
Presentation |
2021-03-03 09:45
A Study of Road Segmentation in Disaster Situations Using UAV Shinta Muto, Jun Ohya (Waseda Univ.) IMQ2020-30 IE2020-70 MVE2020-62 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we propose a system for segmenting road areas from aerial images using machine learning, assuming that fixed-wing UAVs are used to collect information in case of disasters. In the training phase, a deep learning network is trained using the training images to which preprocessing is applied. In the road segmentation phase, unknown images are inputted to the trained model so that road detection results are obtained. We created our own dataset from disaster images taken in Japan, and also created road-hidden images by overlaying disaster images (e.g. landslip) onto roads in non-disaster images. For the training, we used SegNet and DeepLab v3+ as the network structure and different loss functions for comparison. As a result, the highest accuracy was obtained by DeepLab v3+ and Dice Loss. In addition, as a result of applying the proposed method for the road-hidden images, promising results were obtained. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine Learning / Segmentation / Road Detection / SegNet / DeepLab / Loss Function / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 390, IE2020-70, pp. 97-102, March 2021. |
Paper # |
IE2020-70 |
Date of Issue |
2021-02-22 (IMQ, IE, MVE) |
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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IMQ2020-30 IE2020-70 MVE2020-62 |
Conference Information |
Committee |
MVE IMQ IE CQ |
Conference Date |
2021-03-01 - 2021-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
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Paper Information |
Registration To |
IE |
Conference Code |
2021-03-MVE-IMQ-IE-CQ |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study of Road Segmentation in Disaster Situations Using UAV |
Sub Title (in English) |
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Keyword(1) |
Machine Learning |
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Segmentation |
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Road Detection |
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SegNet |
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DeepLab |
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Loss Function |
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1st Author's Name |
Shinta Muto |
1st Author's Affiliation |
Waseda University (Waseda Univ.) |
2nd Author's Name |
Jun Ohya |
2nd Author's Affiliation |
Waseda University (Waseda Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-03 09:45:00 |
Presentation Time |
25 minutes |
Registration for |
IE |
Paper # |
IMQ2020-30, IE2020-70, MVE2020-62 |
Volume (vol) |
vol.120 |
Number (no) |
no.389(IMQ), no.390(IE), no.391(MVE) |
Page |
pp.97-102 |
#Pages |
6 |
Date of Issue |
2021-02-22 (IMQ, IE, MVE) |
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