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Deep Learning for Marine Species Recognition

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Handbook of Deep Learning Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 136))

Abstract

Research on marine species recognition is an important part of the actions for the protection of the ocean environment. It is also an under-exploited application area in the computer vision community. However, with the developments of deep learning, there has been an increasing interest about this topic. In this chapter, we present a comprehensive review of the computer vision techniques for marine species recognition, mainly from the perspectives of both classification and detection. In particular, we focus on capturing the evolution of various deep learning techniques in this area. We further compare the contemporary deep learning techniques with traditional machine learning techniques, and discuss the complementary issues between these two approaches. This chapter examines the attributes and challenges of a number of popular marine species datasets (which involve coral, kelp, plankton and fish) on recognition tasks. In the end, we highlight a few potential future application areas of deep learning in marine image analysis such as segmentation and enhancement of image quality.

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Acknowledgements

This research was partially supported by China Scholarship Council funds (CSC, 201607565016) and Australian Research Council Grants (DP150104251 and DE120102960).

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Authors and Affiliations

  1. The University of Western Australia, Perth, Australia

    Lian Xu, Mohammed Bennamoun & Farid Boussaid

  2. Curtin University, Perth, Australia

    Senjian An

  3. Murdoch University, Perth, Australia

    Ferdous Sohel

Authors
  1. Lian Xu
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  2. Mohammed Bennamoun
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  3. Senjian An
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  4. Ferdous Sohel
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  5. Farid Boussaid
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Corresponding author

Correspondence to Mohammed Bennamoun .

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Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  3. University of Canberra, Bruce, ACT, Australia

    Dharmendra Sharma

  4. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

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Xu, L., Bennamoun, M., An, S., Sohel, F., Boussaid, F. (2019). Deep Learning for Marine Species Recognition. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_7

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