1 research outputs found
Towards Phytoplankton Parasite Detection Using Autoencoders
Phytoplankton parasites are largely understudied microbial components with a
potentially significant ecological impact on phytoplankton bloom dynamics. To
better understand their impact, we need improved detection methods to integrate
phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated
imaging devices usually produce high amount of phytoplankton image data, while
the occurrence of anomalous phytoplankton data is rare. Thus, we propose an
unsupervised anomaly detection system based on the similarity of 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. With this supervised approach and the model trained on plankton
species and anomalies, we were able to reach the highest F1 score of 0.86.
However, the unsupervised approach is expected to be more universal as it can
detect also unknown anomalies and it does not require any annotated anomalous
data that may not be always 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 one which focuses on automated anomaly detection
considering putative phytoplankton parasites or infections