This repository is related to the paper "Strawberry picking point localisation ripeness and weight estimation" accepted for ICRA 2022, PA. In particular it contains the 2 novel datasets presented in the paper.
In the folder annotations/ the annotations for the 2 datasets are present. They splitted into dyson_annotations.zip and SDI_annotations/. Every image has its corresponding json file. In each json file the bounding box, the keypoints and the category (ripe/unripe) for each berry present in the image are annotated.
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Dataset #1: The Dyson dataset can be downloaded from this link.. This dataset is divded into 4 sub-folders, each including a number of data samples. Each data sample is composed of an image witha number of strawberries annotated as follows:
- strawberry_dyson_lincoln_tbd__002_1_pc.ply <- Point cloud
- strawberry_dyson_lincoln_tbd__002_1_label.npy <- Weight
- strawberry_dyson_lincoln_tbd__002_1_rdepth.npy <- Raw depth image
- strawberry_dyson_lincoln_tbd__002_1_bgremoved.png <- RGB image without background
- strawberry_dyson_lincoln_tbd__002_1_odepth.png <- Colorized depth image
- strawberry_dyson_lincoln_tbd__002_1_pdepth.png <- Colorized depth image (different colormap)
- strawberry_dyson_lincoln_tbd__002_1_rgb.png <- RGB image
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Dataset #2: The SDI dataset of images can be instead downloaded from this link.
The materials in this repo can be used under CC-BY-NC-SA license. All data, labels, code and models belong to the Intelligent Manipulation Lab and are freely available for free non-commercial use.
@inproceedings{tafuro2022strawberry,
title={Strawberry picking point localization ripeness and weight estimation},
author={Tafuro, Alessandra and Adewumi, Adeayo and Parsa, Soran and Amir, Ghalamzan E and Debnath, Bappaditya},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
pages={2295--2302},
year={2022},
organization={IEEE}
}