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Semi-Automatic Dataset Generation for Object Detection and Recognition and its Evaluation on Domestic Service Robots
http://hdl.handle.net/10228/00008258
http://hdl.handle.net/10228/00008258ac294324-c679-454e-b976-957dcba95e28
| 名前 / ファイル | ライセンス | アクション |
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10353823.pdf (378.3 kB)
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| Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-05-20 | |||||||||||||
| 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 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Semi-Automatic Dataset Generation for Object Detection and Recognition and its Evaluation on Domestic Service Robots | |||||||||||||
| 言語 | en | |||||||||||||
| その他のタイトル | ||||||||||||||
| その他のタイトル | Semi-automatic Dataset Generation for Object Detection and Recognition and its Evaluation on Domestic Service Robots | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Ishida, Yutaro
× Ishida, Yutaro× 田向, 権
WEKO
6059
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | This paper proposes a method for the semi-automatic generation of a dataset for deep neural networks to perform end-to-end object detection and classification from images, which is expected to be applied to domestic service robots. In the proposed method, the background image of the floor or furniture is first captured. Subsequently, objects are captured from various viewpoints. Then, the background image and the object images are composited by the system (software) to generate images of the virtual scenes expected to be encountered by the robot. At this point, the annotation files, which will be used as teaching signals by the deep neural network, are automatically generated, as the region and category of the object composited with the background image are known. This reduces the human workload for dataset generation. Experiment results showed that the proposed method reduced the time taken to generate a data unit from 167 s, when performed manually, to 0.58 s, i.e., by a factor of approximately 1/287. The dataset generated using the proposed method was used to train a deep neural network, which was then applied to a domestic service robot for evaluation. The robot was entered into the World Robot Challenge, in which, out of ten trials, it succeeded in touching the target object eight times and grasping it four times. | |||||||||||||
| 書誌情報 |
Journal of Robotics and Mechatronics 巻 32, 号 1, p. 245-253, 発行日 2020-02-20 |
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| 出版者 | 富士技術出版 | |||||||||||||
| DOI | ||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.20965/jrm.2020.p0245 | |||||||||||||
| NAID | ||||||||||||||
| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | NAID | |||||||||||||
| 関連識別子 | 130007800559 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 501 | |||||||||||||
| NCID | ||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||
| 収録物識別子 | AA10809998 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||||
| 収録物識別子 | 0915-3942 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 1883-8049 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) Fuji Technlogy Press Ltd | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | domestic service robot | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | object detection and classification | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | dataset generation | |||||||||||||
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| 主題Scheme | Other | |||||||||||||
| 主題 | RoboCup@Home | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | World Robot Challenge | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000641_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10353823 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 8840 | |||||||||||||
