Computer vision and deep learning for fish classification in underwater habitats: A survey

Saleh, Alzayat, Sheaves, Marcus, and Rahimi Azghadi, Mostafa (2022) Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries. (In Press)

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Abstract

Marine scientists use remote underwater image and video recording to survey fish species in their natural habitats. This helps them get a step closer towards understanding and predicting how fish respond to climate change, habitat degradation and fishing pressure. This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment. However, the enormous volume of collected videos makes extracting useful information a daunting and time-consuming task for a human being. A promising method to address this problem is the cutting-edge deep learning (DL) technology. DL can help marine scientists parse large volumes of video promptly and efficiently, unlocking niche information that cannot be obtained using conventional manual monitoring methods. In this paper, we first provide a survey of computer visions (CVs) and DL studies conducted between 2003 and 2021 on fish classification in underwater habitats. We then give an overview of the key concepts of DL, while analysing and synthesizing DL studies. We also discuss the main challenges faced when developing DL for underwater image processing and propose approaches to address them. Finally, we provide insights into the marine habitat monitoring research domain and shed light on what the future of DL for underwater image processing may hold. This paper aims to inform marine scientists who would like to gain a high-level understanding of essential DL concepts and survey state-of-the-art DL-based fish classification in their underwater habitat.

Item ID: 73548
Item Type: Article (Research - C1)
ISSN: 1467-2979
Copyright Information: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2022 The Authors. Fish and Fisheries published by John Wiley & Sons Ltd.
Date Deposited: 30 May 2022 03:00
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 40%
40 ENGINEERING > 4015 Maritime engineering > 401501 Marine engineering @ 10%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems @ 50%
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