Plankton Detection with Adversarial Learning and a Densely Connected Deep Learning Model for Class Imbalanced Distribution

Detecting and classifying the plankton in situ to analyze the population diversity and abundance is fundamental for the understanding of marine planktonic ecosystem. However, the features of plankton are subtle, and the distribution of different plankton taxa is extremely imbalanced in the real marine environment, both of which limit the detection and classification performance of them while implementing the advanced recognition models, especially for the rare taxa. In this paper, a novel plankton detection strategy is proposed combining with a cycle-consistent adversarial network and a densely connected YOLOV3 model, which not only solves the class imbalanced distribution problem of plankton by augmenting data volume for the rare taxa but also reduces the loss of the features in the plankton detection neural network. The mAP of the proposed plankton detection strategy achieved 97.21% and 97.14%, respectively, under two experimental datasets with a difference in the number of rare taxa, which demonstrated the superior performance of plankton detection comparing with other state-of-the-art models. Especially for the rare taxa, the detection accuracy for each rare taxa is improved by about 4.02% on average under the two experimental datasets. Furthermore, the proposed strategy may have the potential to be deployed into an autonomous underwater vehicle for mobile plankton ecosystem observation.

Automated summary

Detecting and classifying plankton in situ is crucial for marine ecosystem understanding, but challenges arise from subtle features and imbalanced taxonomic distribution. A novel strategy combining adversarial network and YOLOV3 model addresses these limitations, achieving high detection performance and potential for deployment in underwater vehicles. This approach significantly improves detection of rare plankton taxa, showcasing superior performance compared to state-of-the-art models.