IFCB phytoplankton anomaly dataset (IFCB-PAD)

Beskrivning

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dataset (https://doi.org/10.23728/b2share.abf913e5a6ad47e6baa273ae0ed6617a) and NOK images consist of unpublished data measured in the 2021 on Utö station using Imaging FlowCytobot (IFCB, McLane Research Laboratories, Inc., U.S., Olson and Sosik, 2007).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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Publiceringsår

2023

Typ av data

Upphovspersoner

Brno University of Technology

Karel Horak Orcid -palvelun logo - Medarbetare

Kaisa Kraft Orcid -palvelun logo - Medarbetare

Lumi Haraguchi Orcid -palvelun logo - Medarbetare

Simon Bilik Orcid -palvelun logo - Upphovsperson, Medarbetare

Daniel Baktrakhanov - Medarbetare

Heikki Kälviäinen Orcid -palvelun logo - Medarbetare

Lasse Lensu Orcid -palvelun logo - Medarbetare

Tuomas Eerola Orcid -palvelun logo - Medarbetare, Utgivare

Conny Sjöqvist Orcid -palvelun logo - Medarbetare

Karin Madsen - Medarbetare

Jonna Kangas - Medarbetare

Silke Van den Wyngaert Orcid -palvelun logo - Medarbetare

Projekt

Övriga uppgifter

Vetenskapsområden

Data- och informationsvetenskap; Miljövetenskap

Språk

engelska

Öppen tillgång

Öppet

Licens

Creative Commons Attribution 4.0 International (CC BY 4.0)

Nyckelord

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

Ämnesord

optisk läsning, parasiter, anomalier

Temporal täckning

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