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    Implications of single-stage deep learning networks in real-time zooplankton identification

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    10906.pdf (2.474Mb)
    Date
    2023
    Author
    Ansari, S.
    Desai, D.V.
    Saad, A.
    Stahl, A.
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    Abstract
    Zooplankton are key ecological components of the marine food web. Currently, laboratory-based methods of zoo-plankton identification are manual, time-consuming, prone to human error and require expert taxonomists. Therefore, alternative methods are needed. In this study, we describe, implement and compare the performance of six state-of-the-art single-stage deep learning models for automated zooplankton identification. The highest prediction accuracy achieved is 99.50%. The fastest detection speed is 285 images per second, making the models suitable for real-time zooplankton classification. We validate the predictions of the generated models on unseen images. The results demonstrate the capabilities of the latest deep learning models in zooplankton identification .
    URI
    https://doi.org/10.18520/cs/v125/i11/1259-1266
    http://drs.nio.res.in/drs/handle/2264/9175
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