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BILÍK, Š. BAKTRAKHANOV, D. EEROLA, T. HARAGUCHI, L. KRAFT, K. VAN DEN WYNGAERT, S. KANGAS, J. SJÖQVIST, C. MADSEN, K. LENSU, L. KÄLVIÄINEN, H. HORÁK, K.
Originální název
Towards Phytoplankton Parasite Detection Using Autoencoders
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological influence on phytoplankton bloom dynamics. To better understand the impact of phytoplankton parasites, improved detection methods are needed to integrate phytoplankton parasite interactions into monitoring of aquatic ecosystems. Automated imaging devices commonly produce vast amounts of phytoplankton image data, but the occurrence of anomalous phytoplankton data in such datasets is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity between the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN-based object detector. Using this supervised approach and the model trained on plankton species and anomalies, we were able to reach a highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can also detect unknown anomalies and it does not require any annotated anomalous data that may not always be available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles or air bubble detection, our paper is, according to our best knowledge, the first that focuses on automated anomaly detection considering putative phytoplankton parasites or infections.
Klíčová slova
Phytoplankton anomalies, Phytoplankton parasites, Anomaly detection, Autoencoders, Object detection, Faster R-CNN
Autoři
BILÍK, Š.; BAKTRAKHANOV, D.; EEROLA, T.; HARAGUCHI, L.; KRAFT, K.; VAN DEN WYNGAERT, S.; KANGAS, J.; SJÖQVIST, C.; MADSEN, K.; LENSU, L.; KÄLVIÄINEN, H.; HORÁK, K.
Vydáno
13. 9. 2023
Nakladatel
Springer
ISSN
1432-1769
Periodikum
Machine Vision and Applications
Ročník
34
Číslo
6
Stát
Spojené státy americké
Strany od
1
Strany do
18
Strany počet
URL
https://link.springer.com/article/10.1007/s00138-023-01450-x
Plný text v Digitální knihovně
http://hdl.handle.net/11012/214455
BibTex
@article{BUT184624, author="Šimon {Bilík} and Daniel {Baktrakhanov} and Tuomas {Eerola} and Lumi {Haraguchi} and Kaisa {Kraft} and Silke {Van den Wyngaert} and Jonna {Kangas} and Conny {Sjöqvist} and Karin {Madsen} and Lasse {Lensu} and Heikki {Kälviäinen} and Karel {Horák}", title="Towards Phytoplankton Parasite Detection Using Autoencoders", journal="Machine Vision and Applications", year="2023", volume="34", number="6", pages="1--18", doi="10.1007/s00138-023-01450-x", issn="1432-1769", url="https://link.springer.com/article/10.1007/s00138-023-01450-x" }