VINEyard Piacenza Image Collections - VINEPICs

For a detailed description of this dataset, based on the Datasheets for Datasets (Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92.), check the VINEPICs_datasheet.md file. For what purpose was the dataset created? VINEPICs was developed specifically for the purpose of detecting grape bunches in RGB images and facilitating tasks such as object detection, semantic segmentation, and instance segmentation. The detection of grape bunches serves as the initial phase in an analysis pipeline designed for vine plant phenotyping. The dataset encompasses a wide range of lighting conditions, camera orientations, plant defoliation levels, species variations, and cultivation methods. Consequently, this dataset presents an opportunity to explore the influence of each source of variability on grape bunch detection. What do the instances that comprise the dataset represent? The dataset consists of RGB images showcasing various species of vine plants. Specifically, the images represent three different Vitis vinifera varieties: - Red Globe, a type of table grape - Cabernet Sauvignon, a red wine grape - Ortrugo, a white wine grape These images have been collected over different years and dates at the vineyard facility of Università Cattolica del Sacro Cuore in Piacenza, Italy. You can find the images stored in the "data/images" directory, organized into subdirectories based on the starting time of data collection, indicating the day (and, if available, the approximate time in minutes). Images collected in 2022 are named using timestamps with nanosecond precision. Is there a label or target associated with each instance? Each image has undergone manual annotation using the Computer Vision Annotation Tool (CVAT) (https://github.com/opencv/cvat). Grape bunches have been meticulously outlined with polygon annotations. These annotations belong to a single class, "bunch," and have been saved in a JSON file using the COCO Object Detection format, including segmentation masks (https://cocodataset.org/#format-data). What mechanisms or procedures were used to collect the data? The data was collected using a D435 Intel Realsense camera, which was mounted on a four-wheeled skid-steering robot. The robot was teleoperated during the data collection process. The data was recorded by streaming the camera's feed into rosbag format. Specifically, the camera was connected via a USB 3.0 interface to a PC running Ubuntu 18.04 and ROS Melodic.