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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 management...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685673/ https://www.ncbi.nlm.nih.gov/pubmed/31406905 http://dx.doi.org/10.1016/j.dib.2019.104289 |
Sumario: | 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 RGB-DS 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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