Gené-Mola Jordi, Vilaplana Verónica, Rosell-Polo Joan R, Morros Josep-Ramon, Ruiz-Hidalgo Javier, Gregorio Eduard
Research Group in AgroICT & Precision Agriculture, Department of Agricultural and Forest Engineering, Universitat de Lleida (UdL) - Agrotecnio Center, Lleida, Catalonia, Spain.
Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain.
Data Brief. 2019 Jul 19;25:104289. doi: 10.1016/j.dib.2019.104289. eCollection 2019 Aug.
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.
本文包含与题为《使用RGB-D相机及其辐射测量能力进行水果检测的多模态深度学习》[1]的研究文章相关的数据。可靠的水果检测和定位系统的开发对于未来高价值作物的可持续农艺管理至关重要。RGB-D传感器已显示出在水果检测和定位方面的潜力，因为它们能提供带有颜色数据的三维信息。然而，缺乏大量数据集是利用这些传感器的一个障碍。本文介绍了KFuji RGB-DS数据库，该数据库由使用微软Kinect v2（微软，美国华盛顿州雷德蒙德）拍摄的967张富士苹果在树上的多模态图像组成。每张图像包含来自3种不同模态的信息：颜色（RGB）、深度（D）和距离校正红外强度（S）。水果的真实位置是手动标注的，在整个数据集中总共标注了12839个苹果。当前数据集可在http://www.grap.udl.cat/publicacions/datasets.html上公开获取。
