Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

TL;DR

This survey reviews deep learning techniques for underwater fish classification, highlighting challenges, recent advances, and future directions to aid marine habitat monitoring and conservation efforts.

Contribution

It provides a comprehensive overview of deep learning applications in underwater fish classification and discusses key challenges and potential solutions in this domain.

Findings

Deep learning enables efficient processing of large underwater video datasets.

Current challenges include image quality and variability in underwater environments.

Future research directions involve improving model robustness and real-time processing.

Abstract

Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict 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. 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 provide an overview of the key concepts of DL, while presenting a survey of literature on fish habitat monitoring with a focus on underwater fish classification. 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 a wide range of readers from marine scientists who would like to apply DL in their research to computer scientists who would like to survey state-of-the-art DL-based underwater fish habitat monitoring literature.