Gené-Mola Jordi, Sanz-Cortiella Ricardo, Rosell-Polo Joan R, Morros Josep-Ramon, Ruiz-Hidalgo Javier, Vilaplana Verónica, 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. 2020 Apr 21;30:105591. doi: 10.1016/j.dib.2020.105591. eCollection 2020 Jun.
The present dataset contains colour images acquired in a commercial Fuji apple orchard ( Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled "Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry" [1]. The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. With that, this is the first dataset for fruit detection containing images acquired in a motion sequence to build the 3D model of the scanned trees with SfM and including the corresponding 2D and 3D apple location annotations. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data.
本数据集包含在富士苹果商业果园（富士品种，Borkh. cv. Fuji）中采集的彩色图像，用于通过运动结构（SfM）摄影测量法重建11棵树的三维模型。本文提供的数据与题为《使用实例分割神经网络和运动结构摄影测量法进行果实检测和三维定位》的研究文章相关。富士-SfM数据集包括：（1）一组288张彩色图像以及用于训练实例分割神经网络（如Mask-RCNN）的相应注释（苹果分割掩码）；（2）一组582张定义场景运动序列的图像，该序列用于通过SfM生成包含1455个苹果的11棵富士苹果树的三维模型；（3）扫描场景的三维点云以及全局坐标中相应苹果位置的地面真值。据此，这是第一个用于果实检测的数据集，包含在运动序列中采集的图像，以利用SfM构建扫描树木的三维模型，并包括相应的二维和三维苹果位置注释。这些数据可用于开发、训练和测试基于RGB图像、彩色点云或这两种数据类型组合的果实检测算法。
