Abstract
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.
Highlights
As a main component of the marine ecosystem, plankton plays an important role in both the global marine carbon cycle and early warning ahead of natural disasters [1,2]
Dataset Description in this work, which is provided by Woods Hole Oceanographic Institution with an ImagA large scale and fine-grained dataset for plankton named WHOI-Plankton are used ing FlowCytobot (IFCB) to imaging plankton since 2006 [21]
The WHOI-Plankton dataset in this work, which is provided by Woods Hole Oceanographic Institution with an Imaging comprises over 3.4 million expert-labeled images covering 100 taxa
Summary
As a main component of the marine ecosystem, plankton plays an important role in both the global marine carbon cycle and early warning ahead of natural disasters [1,2]. The plankton with a high-density distribution will affect the performance of the detecting sensors such as sonar since the acoustic transmission is impeded. The research on the comprehensive understanding of the distribution and abundance of the plankton in the marine environment is a focus issue for both ecologists and engineers. In the past decades and even the core ways of plankton sampling are mainly employing traditional tools such as filters, pumps and nets. The collected samples are investigated manually employing expert knowledge in the laboratory environment. The samples are easy to be destroyed during the sampling and investigation, especially for the fragile gelatinous plankton organisms, which would result in a wrong conclusion. This way of plankton sampling and investigation is labor-intensive and time-consuming
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