| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2018-03-01 |
| 書誌情報 |
en : IEEE Transactions on Parallel and Distributed Systems
巻 28,
号 12,
p. 3458-3471,
発行日 2017-08-15
|
| タイトル |
|
|
タイトル |
Eyes in the Dark: Distributed Scene Understanding for Disaster Management |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Distributed artificial intelligence |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
scene understanding system |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
disaster management |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
李, 良知
太田, 香
董, 冕雄
BORJIGIN, Wuyunzhaola
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision research, traditional approaches cannot be applied to disaster-handling robots directly due to some limitations. In this paper, we focus on the 3D robotic perception, and propose a view-invariant Convolutional Neural Network (CNN) Model for scene understanding in disaster scenarios. The proposed system is highly distributed and parallel, which is of great help to improve the efficiency of network training. In our system, two individual CNNs are used to, respectively, propose objects from input data and classify their categories. We attempt to overcome the difficulties and restrictions caused by disasters using several specially-designed multi-task loss functions. The most significant advantage in our work is that the proposed method can learn a view-invariant feature with no requirement on RGB data, which is essential for harsh, disordered and changeable environments. Additionally, an effective optimization algorithm to accelerate the learning process is also included in our work. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks. |
|
言語 |
en |
| 出版者 |
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出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1109/TPDS.2017.2740294 |
|
URL |
https://doi.org/10.1109/TPDS.2017.2740294 |
| DOI |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1109/TPDS.2017.2740294 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1045-9219 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
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|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
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内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
IEEETPDS_28_12_3458_3471 (4.8 MB)
