Toward phytoplankton parasite detection using autoencoders
Description
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.
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02 Bilik et al 2023 Machine Vision and Applications.pdf
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