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Deep learning for marine species recognition

Xu, L., Bennamoun, M., An, S., Sohel, F. and Boussaid, F. (2019) Deep learning for marine species recognition. In: Balas, V.E., Roy, S.S., Sharma, D. and Samui, P., (eds.) Advances in Computational Intelligence. Springer, pp. 129-145.

Link to Published Version: https://doi.org/10.1007/978-3-030-11479-4_7
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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.

Item Type: Book Chapter
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer
Copyright: © 2019 Springer Nature Switzerland AG
Other Information: Series title: Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, Vol. 136.
URI: http://researchrepository.murdoch.edu.au/id/eprint/44570
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