GRAPE_DETECTION_DATASET

The dataset consists of three distinct groups of RGB images, each designed for training and validation purposes. The groups were separated based on the camera sensor used to collect the data. The first set contains 151 images, with 101 for training and 50 for validation. The first group is contained in the C1 folder. These images were taken with a ZED Mini camera held by hand at a constant distance of one meter from the vineyard. The resolution varies within this set, with images available in 1024 x 1024 or 2208 x 1242 pixels. The second collection encompasses 236 RGB images, of which 155 are designated for training and 81 for validation. The second group is contained in the C2 and C3 folders. These images were taken with an Intel Realsense D435 camera, capturing shots of the grapes positioned 20 to 50 cm away from the camera. The resolution for this set is standardized at 1920 x 1080 pixels, with the camera securely mounted on one of the robot's arms. The third and final group consists of 1320 images, with 1092 allocated for training and 228 for validation. This group is splitted in the I1, I2 and I3 folders. Captured using a ZED2 camera, these images were obtained by an inspection platform located 1 meter away from the vineyard, maintaining a resolution of 1920 x 1080 pixels. Together, these diverse sets form a comprehensive RGB dataset for the development and assessment of vineyard-related algorithms. This dataset used for detecting the grapes in the images. All of the data were collected at Ktima Gerovassiliou in Epanomi, Thessaloniki. Below, there are six separate folders with the training data (which can be merged into one) and one folder containing the validation data. Every image has a txt file containing the image's grapes annotations. The first number represents the annotation's class (0 for grape), the second and third numbers are the normalized (in the image's shape) coordinates of the grape's bounding box up left edge, and the fourth and fifth numbers are the bounding box's width and height accordingly.本数据集包含三组独立的RGB图像，所有图像均用于模型的训练与验证环节。三组数据集按照采集所用的相机传感器型号进行划分。 第一组数据集共含151张图像，其中101张用于训练，50张用于验证，存储于C1文件夹中。该组图像由手持ZED Mini相机采集，相机与葡萄园保持1米的固定距离，图像分辨率不统一，涵盖1024×1024与2208×1242两种规格。 第二组数据集共涵盖236张RGB图像，其中155张用于训练，81张用于验证，存储于C2与C3文件夹中。该组图像由Intel Realsense D435相机采集，拍摄时葡萄与相机的间距为20至50厘米，分辨率统一为1920×1080像素，相机牢固安装于机械臂之上。 第三组即最后一组数据集共包含1320张图像，其中1092张分配用于训练，228张用于验证，该组数据存储于I1、I2与I3三个文件夹中。该组图像由ZED2相机采集，搭载相机的巡检平台与葡萄园间距为1米，图像分辨率固定为1920×1080像素。 上述三组多样化的数据集共同构成了一套完备的RGB数据集，可用于开发与评估面向葡萄园场景的相关算法，本数据集专为图像中的葡萄检测任务构建。所有数据均采集自希腊塞萨洛尼基市埃帕诺米地区的Ktima Gerovassiliou酒庄。 数据集包含六个独立的训练数据文件夹（可合并为一个总文件夹）以及一个验证数据文件夹。每张图像均对应一个TXT格式的标注文件，用于存储该图像内的葡萄标注信息：首个数字代表标注类别（0代表葡萄），第二、第三个数字为归一化后的葡萄边界框左上角坐标（归一化基于图像自身尺寸），第四、第五个数字则分别为边界框的宽度与高度。

搜集汇总

背景与挑战

背景概述

GRAPE_DETECTION_DATASET是一个专为葡萄检测算法开发设计的RGB图像数据集，包含三组不同相机拍摄的图像，总计1707张（训练1348张，验证359张），覆盖多种拍摄条件和分辨率，并附带详细的葡萄边界框标注信息。数据集由BACCHUS H2020项目资助，数据采集于希腊塞萨洛尼基的Ktima Gerovassiliou。