Paper Abstract and Keywords |
Presentation |
2022-06-09 17:15
Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data Kaito Arase, Zhijian Wu, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2022-10 CCS2022-10 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The authors have recently conducted training of Convolutional Neural Networks (CNNs) on OpenStreetMap images each of which is labeled as ``danger'' or ``safe'' based on traffic accident data. Although the trained CNNs determine whether each area is danger or safe from the map image correctly with a high probability, the reason for this and the basis for their decisions are not clear. In this report, we use a method called Grad-CAM to visualize the basis of CNNs' decisions after learning map images as described above. The visualization result of the Grad-CAM depends on the convolutional layer of the CNN it is applied. The closer the layer is to the output layer, the better the features can be captured, but the resolution of the visualization is lower. Conversely, the closer the layer is to the input layer, the higher the resolution of the visualization, but the less well the features are captured. By analyzing the visualization results of the Grad-CAM for different convolutional layers of different models, we show that there certainly exist convolutional layers suitable for visualization, and clarify some characteristics of the trained CNNs when making their decisions. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
deep learning / map image / OpenStreetMap / Grad-CAM / traffic accident data / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 65, NLP2022-10, pp. 46-51, June 2022. |
Paper # |
NLP2022-10 |
Date of Issue |
2022-06-02 (NLP, CCS) |
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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NLP2022-10 CCS2022-10 |
Conference Information |
Committee |
CCS NLP |
Conference Date |
2022-06-09 - 2022-06-10 |
Place (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
NLP |
Conference Code |
2022-06-CCS-NLP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data |
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Keyword(1) |
deep learning |
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map image |
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OpenStreetMap |
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Grad-CAM |
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traffic accident data |
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1st Author's Name |
Kaito Arase |
1st Author's Affiliation |
Okayama University (Okayama Univ.) |
2nd Author's Name |
Zhijian Wu |
2nd Author's Affiliation |
Okayama University (Okayama Univ.) |
3rd Author's Name |
Tsuyoshi Migita |
3rd Author's Affiliation |
Okayama University (Okayama Univ.) |
4th Author's Name |
Norikazu Takahashi |
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Okayama University (Okayama Univ.) |
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Speaker |
Author-1 |
Date Time |
2022-06-09 17:15:00 |
Presentation Time |
25 minutes |
Registration for |
NLP |
Paper # |
NLP2022-10, CCS2022-10 |
Volume (vol) |
vol.122 |
Number (no) |
no.65(NLP), no.66(CCS) |
Page |
pp.46-51 |
#Pages |
6 |
Date of Issue |
2022-06-02 (NLP, CCS) |
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