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Real-Time Instance Segmentation and Point Cloud Extraction for Japanese Food
http://hdl.handle.net/10228/00008280
http://hdl.handle.net/10228/0000828029475040-8243-49fe-be98-a7ff3475586f
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
sociorobo_4.pdf (556.9 kB)
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| Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-05-27 | |||||||||||||
| 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 | Real-Time Instance Segmentation and Point Cloud Extraction for Japanese Food | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Yarnchalothorn, Suthiwat
× Yarnchalothorn, Suthiwat× Damrongplasit, Nattapol× Chumkamon, Sakmongkon× 林, 英治
WEKO
30038
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Innovation in technology is playing an important role in the development of food industry, as is evidenced by the growing number of food review and food delivery applications. Similarly, it is expected that the process of producing and packaging food itself will become increasingly automated through the use of a robotic system. The shift towards food automation would help ensure quality control of food products and improve production line efficiency, leading to reduced cost and higher profit margin for restaurants and factories. One key enabler for such automated system is the ability to detect and classify food object with great accuracy and speed. In this study, we explore real-time food object segmentation using stereo depth sensing camera mounted on a robotic arm system. Instance segmentation on Japanese food dataset is used to classify food objects at a pixel-level using Cascade Mask R-CNN deep learning model. Additionally, depth information from the sensor is extracted to generate a 3D point cloud of the food object and its surroundings. When combined with the segmented 2D RGB image, a segmented 3D point cloud of the food object can be constructed, which will help facilitate food automation operation such as precision grasping of food object with numerous shapes and sizes. | |||||||||||||
| 備考 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | The Society of Instrument and Control Engineers (SICE) Annual Conference 2020 (SICE2020), September 23-26, 2020, Chiang Mai, Thailand （新型コロナ感染拡大に伴い、現地開催中止） | |||||||||||||
| 書誌情報 |
Proceedings of the SICE Annual Conference 2020 p. 338-342, 発行日 2020-09 |
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| 出版社 | ||||||||||||||
| 出版者 | 計測自動制御学会 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-4-907764-68-5 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2020 SICE | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Instance Segmentation | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Cascade R-CNN | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | 3D Point Cloud | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Food Automation | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 8864 | |||||||||||||
