On-tree mango instance segmentation dataset

Dataset created for on-tree mango fruit detection and segmentation as a part of mango fruit size estimation study. Image datasets were prepared for training of Convolutional Neural Network (CNN) based instance segmentation model and annotated using VGG Image Annotation tool (Dutta & Zisserman 2019) with polygon region annotation. Two folders contain cropped images and COCO style JSON annotation files and randomly separated into training and test image sets. Images were taken at nighttime with the use of artificial light (LED light panel), using Azure Kinect RGB-D camera and Basler ace acA2440-75uc RGB camera.Datasets contain images from Honey Gold and Keitt mango cultivars and folders are organized as:Folder 1 (individual-mango-snips) - contains tiled-images and annotation file - A total of 542 (train + test) tiled images of 640 x 540 pixels.Folder 2 (tiled-images) - individual-mango-snips - Total 1200 (train + test) snips of variable dimensions (<150 pixels)For anyone intended to use the dataset for their research purpose, please cite following related journal paper:Neupane, C.; Koirala, A.; Walsh, K.B. In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation. Horticulturae 2022, 8, 1223. https://doi.org/10.3390/horticulturae8121223

本数据集专为树上芒果果实检测与分割任务构建，作为芒果果实尺寸估测研究的组成部分。本图像数据集用于训练基于卷积神经网络（Convolutional Neural Network, CNN）的实例分割模型，并通过VGG图像标注工具（VGG Image Annotation Tool，Dutta与Zisserman，2019）完成多边形区域标注。数据集包含两个文件夹，分别存储裁剪图像与COCO风格JSON标注文件，且所有图像已随机划分为训练集与测试集。图像均在夜间借助人工光源（LED灯板）采集，采集设备为Azure Kinect RGB-D相机与Basler ace acA2440-75uc RGB相机。本数据集涵盖Honey Gold与Keitt两个芒果品种的图像，文件夹组织方式如下：文件夹1（individual-mango-snips）——包含分块图像与标注文件，共计542张（训练集+测试集）640×540像素的分块图像。文件夹2（tiled-images）——individual-mango-snips——共计1200张（训练集+测试集）尺寸可变（小于150像素）的裁剪片段。若研究者将本数据集用于科研工作，请引用以下期刊论文：Neupane, C.; Koirala, A.; Walsh, K.B. 《果园内芒果果实尺寸估测：1. 基于机器视觉的实时估测方法对比》，Horticulturae 2022, 8, 1223. https://doi.org/10.3390/horticulturae8121223