IFCB phytoplankton anomaly dataset (IFCB-PAD)

Description

IFCB phytoplankton anomaly dataset (IFCB-PAD) contains over 6200 manually annotated and expert-validated phytoplankton images (9 plankton classes) with anomalies such as parasites. The dataset with bounding box annotations is available in both COCO and YOLO format. OK images (no anomalies) are derived from SYKE-plankton_IFCB_2022 dataset (https://doi.org/10.23728/b2share.abf913e5a6ad47e6baa273ae0ed6617a) and NOK images consist of unpublished data measured in the 2021 on Utö station using Imaging FlowCytobot (IFCB, McLane Research Laboratories, Inc., U.S., Olson and Sosik, 2007). The plankton class list: - Aphanizomenon - Centrales - Dolichospermum - Chaetocero - Nodularia - Pauliella - Peridiniella Chain - Peridiniella Single - Skeletonem If you use this dataset in your research, we kindly ask that you reference the following paper: Bilik, S., Baktrakhanov, D., Eerola, T., Haraguchi, L., Kraft, K., Wyngaert, S.V.D., Kangas, J., Sjöqvist, C., Madsen, K., Lensu, L. and Kälviäinen, H., 2023. Towards Phytoplankton Parasite Detection Using Autoencoders. arXiv preprint arXiv:2303.08744.
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Year of publication

2023

Type of data

Authors

Brno University of Technology

Karel Horak Orcid -palvelun logo - Contributor

Kaisa Kraft Orcid -palvelun logo - Contributor

Lumi Haraguchi Orcid -palvelun logo - Contributor

Tuomas Eerola Orcid -palvelun logo - Publisher, Contributor

Simon Bilik Orcid -palvelun logo - Creator, Contributor

Daniel Baktrakhanov - Contributor

Heikki Kälviäinen Orcid -palvelun logo - Contributor

Lasse Lensu Orcid -palvelun logo - Contributor

Jonna Kangas - Contributor

Silke Van den Wyngaert Orcid -palvelun logo - Contributor

Conny Sjöqvist Orcid -palvelun logo - Contributor

Karin Madsen - Contributor

Project

Other information

Fields of science

Computer and information sciences; Environmental sciences

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

Computer vision, object detection, phytoplankton, Anomaly detection, Phytoplankton parasites, Plankton imaging

Subject headings

Temporal coverage

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