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Part 1: Document Description
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Citation |
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Title: |
BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data |
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Identification Number: |
doi:10.60507/FK2/NUMVO1 |
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Distributor: |
bonndata |
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Date of Distribution: |
2025-10-06 |
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Version: |
1 |
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Bibliographic Citation: |
Guclu, Esra; Halstead, Michael, 2025, "BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data", https://doi.org/10.60507/FK2/NUMVO1, bonndata, V1 |
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Citation |
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Title: |
BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data |
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Identification Number: |
doi:10.60507/FK2/NUMVO1 |
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Authoring Entity: |
Guclu, Esra (University of Bonn) |
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Halstead, Michael (University of Bonn) |
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Other identifications and acknowledgements: |
Agricultural Robotics Group |
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Other identifications and acknowledgements: |
Guclu Esra |
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Other identifications and acknowledgements: |
Halstead Michael |
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Other identifications and acknowledgements: |
McCool Chris |
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Other identifications and acknowledgements: |
Denman Simon |
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Distributor: |
bonndata |
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Access Authority: |
Güclü, Esra |
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Depositor: |
Güclü, Esra |
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Date of Deposit: |
2025-09-29 |
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Date of Distribution: |
2025-09-29 |
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Holdings Information: |
https://doi.org/10.60507/FK2/NUMVO1 |
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Study Scope |
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Keywords: |
Agricultural Sciences |
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Abstract: |
Accurate monitoring of crop phenotypic traits is essential for efficient farm management and automation in agriculture. Multi-object tracking (MOT) and video instance segmentation (VIS) offer promising approaches to enhance agricultural robotic vision systems, yet a major limitation is the scarcity of high-quality spatial-temporal datasets. We introduce BUP-ST20, a novel weakly labelled spatial-temporal dataset for sweet pepper tracking and segmentation captured on a robotic platform. BUP-ST20 contains 16,240 images from 275 sequences, each with bounding boxes, instance segmentation masks, and temporal identities.The dataset has weakly labelled training and validation sets, while the evaluation set includes 3810 frames with hand-labelled ground truth annotations. |
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Date of Collection: |
2020-09-24-2020-10-01 |
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Kind of Data: |
Images |
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Kind of Data: |
Numerical |
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Kind of Data: |
Spreadsheet |
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Kind of Data: |
Structured |
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Kind of Data: |
Qualitative |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/licenses/by/4.0">CC BY 4.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Guclu E, Halstead M, Denman S, McCool C. Weakly labelled spatial-temporal sweet pepper data: Enabling higher quality detection, segmentation, and tracking. The International Journal of Robotics Research. 2025;0(0). doi:10.1177/02783649251379093 |
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Identification Number: |
10.1177/02783649251379093 |
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Bibliographic Citation: |
Guclu E, Halstead M, Denman S, McCool C. Weakly labelled spatial-temporal sweet pepper data: Enabling higher quality detection, segmentation, and tracking. The International Journal of Robotics Research. 2025;0(0). doi:10.1177/02783649251379093 |
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bupst20_annotations.tar.gz |
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application/gzip |
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bupst20_depth.tar.gz.aa |
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audio/x-pn-audibleaudio |
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bupst20_depth.tar.gz.ab |
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application/octet-stream |
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bupst20_odometry.tar.gz |
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application/gzip |
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bupst20_rgb.tar.gz.aa |
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audio/x-pn-audibleaudio |
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bupst20_rgb.tar.gz.ab |
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application/octet-stream |
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bupst20_rgb.tar.gz.ac |
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application/pkix-attr-cert |
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bupst20_rgb.tar.gz.ad |
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application/octet-stream |
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bupst20_rgb.tar.gz.ae |
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application/octet-stream |
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bupst20_rgb.tar.gz.af |
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application/octet-stream |
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bupst20_rgb.tar.gz.ag |
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image/x-applix-graphics |
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cam_params.yaml |
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application/x-yaml |
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dataset_structure.md |
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text/markdown |
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how_to_use_BUPST20.md |
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text/markdown |
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ReadMe.txt |
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Notes: |
text/plain |
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train_valid_eval_splits.yaml |
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Notes: |
application/x-yaml |