KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
This article contains data related to the research article entitle 'Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities' [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic...
| Autores: | , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2019 |
| País: | España |
| Recursos: | Universitat de Lleida (UdL) |
| Repositório: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/66667 |
| Acesso em linha: | https://doi.org/10.1016/j.dib.2019.104289 http://hdl.handle.net/10459.1/66667 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Multi-modal dataset fruit detection Depth cameras RGB-D cameras |
| Resumo: | This article contains data related to the research article entitle 'Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities' [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGBDS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html. |
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