Deep learning based segmentation of fish in noisy forward looking MBES images

Jesper Haahr Christensen*, Lars Valdemar Mogensen, Ole Ravn

*Corresponding author for this work

    Research output: Contribution to journalConference articleResearchpeer-review

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    In this work, we investigate a Deep Learning (DL) approach to fish segmentation in a small dataset of noisy low-resolution images generated by a forward-looking multibeam echosounder (MBES). We build on recent advances in DL and Convolutional Neural Networks (CNNs) for semantic segmentation and demonstrate an end-to-end approach for a fish/non-fish probability prediction for all range-azimuth positions projected by an imaging sonar. We use self-collected datasets from the Danish Sound and the Faroe Islands to train and test our model and present techniques to obtain satisfying performance and generalization even with a low-volume dataset. We show that our model proves the desired performance and has learned to harness the importance of semantic context and take this into account to separate noise and non-targets from real targets. Furthermore, we present techniques to deploy models on low-cost embedded platforms to obtain higher performance fit for edge environments - where compute and power are restricted by size/cost - for testing and prototyping.

    Original languageEnglish
    Book seriesIFAC-PapersOnLine
    Issue number2
    Pages (from-to)14546-14551
    Publication statusPublished - 2020
    Event21st IFAC World Congress 2020 - Berlin, Germany
    Duration: 12 Jul 202017 Jul 2020


    Conference21st IFAC World Congress 2020

    Bibliographical note

    Publisher Copyright:
    Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license


    • Autonomous underwater vehicle (AUV)
    • Deep learning
    • Fish monitoring
    • Multibeam echosounder (mbes) Imaging
    • Semantic segmentation
    • Sonar imaging


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