Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

• URL: http://arxiv.org/abs/2206.05394v1
• Date: Sat, 11 Jun 2022 01:59:54 GMT
• Title: Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey
• Authors: Alzayat Saleh, Marcus Sheaves, Dean Jerry, and Mostafa Rahimi Azghadi
• Abstract summary: Deep learning (DL) is a cutting-edge AI technology that has demonstrated unprecedented performance in analysing visual data. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring.
• Score: 1.9249287163937976
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. DL is a cutting-edge AI technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.

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