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Deep Learning on Underwater Marine Object Detection: A Survey

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10617))

Abstract

Deep learning, also known as deep machine learning or deep structured learning based techniques, have recently achieved tremendous success in digital image processing for object detection and classification. As a result, they are rapidly gaining popularity and attention from the computer vision research community. There has been a massive increase in the collection of digital imagery for the monitoring of underwater ecosystems, including seagrass meadows. This growth in image data has driven the need for automatic detection and classification using deep neural network based classifiers. This paper systematically describes the use of deep learning for underwater imagery analysis within the recent past. The analysis approaches are categorized according to the object of detection, and the features and deep learning architectures used are highlighted. It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass.

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Acknowledgement

Authors would like to acknowledge that this work is done with the support of Australian Government Research Training Program Scholarship and Edith Cowan University ECR grant.

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Authors and Affiliations

  1. School of Science, Edith Cowan University, Joondalup, WA, 6027, Australia

    Md. Moniruzzaman, Syed Mohammed Shamsul Islam & Paul Lavery

  2. School of Computer Science and Software Engineering, University of Western Australia, Crawley, WA, 6009, Australia

    Syed Mohammed Shamsul Islam & Mohammed Bennamoun

Authors
  1. Md. Moniruzzaman
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  2. Syed Mohammed Shamsul Islam
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  3. Mohammed Bennamoun
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  4. Paul Lavery
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Corresponding author

Correspondence to Syed Mohammed Shamsul Islam .

Editor information

Editors and Affiliations

  1. DGA, Paris, France

    Jacques Blanc-Talon

  2. University of Antwerp, Antwerp, Belgium

    Rudi Penne

  3. Ghent University - imec, Ghent, Belgium

    Wilfried Philips

  4. CSIRO Data 61, Canberra, Aust Capital Terr, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Moniruzzaman, M., Islam, S.M.S., Bennamoun, M., Lavery, P. (2017). Deep Learning on Underwater Marine Object Detection: A Survey. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-70353-4_13

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70353-4

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