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

arXiv: https://arxiv.org/abs/2203.06951 · HTML: https://mlsanity.com/paper/2203.06951/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 151 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Regularization for Deep Learning: A Taxonomy](https://mlsanity.com/paper/1710.10686/) (2017)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis](https://mlsanity.com/paper/2008.12603/) (2020)
- [Deep Learning in Bioinformatics](https://mlsanity.com/paper/1603.06430/) (2016)
- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [Deep learning for smart fish farming: applications, opportunities and challenges](https://mlsanity.com/paper/2004.11848/) (2020)
- [Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey](https://mlsanity.com/paper/2012.06712/) (2020)
- [Deep learning with self-supervision and uncertainty regularization to count fish in underwater images](https://mlsanity.com/paper/2104.14964/) (2021)
- [Temperate Fish Detection and Classification: a Deep Learning based Approach](https://mlsanity.com/paper/2005.07518/) (2020)
- [Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning](https://mlsanity.com/paper/1805.10106/) (2018)
- [Underwater Fish Detection with Weak Multi-Domain Supervision](https://mlsanity.com/paper/1905.10708/) (2019)
- [Counting Fish and Dolphins in Sonar Images Using Deep Learning](https://mlsanity.com/paper/2007.12808/) (2020)
- [Affinity LCFCN: Learning to Segment Fish with Weak Supervision](https://mlsanity.com/paper/2011.03149/) (2020)
- [A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images](https://mlsanity.com/paper/2109.14919/) (2021)
- [A Feature Learning and Object Recognition Framework for Underwater Fish Images](https://mlsanity.com/paper/1603.01696/) (2016)

## Built on by (most cited first, 14 of 14)

- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (2023)
- [AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis](https://mlsanity.com/paper/2502.01785/) (2025)
- [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://mlsanity.com/paper/2206.05394/) (2022)
- [Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach](https://mlsanity.com/paper/2206.05390/) (2022)
- [How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2208.10662/) (2022)
- [Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data](https://mlsanity.com/paper/2411.15602/) (2024)
- [MATANet: A Multi-context Attention and Taxonomy-Aware Network for Fine-Grained Underwater Recognition of Marine Species](https://mlsanity.com/paper/2601.03729/) (2026)
- [A lightweight Transformer-based model for fish landmark detection](https://mlsanity.com/paper/2209.05777/) (2022)
- [Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization](https://mlsanity.com/paper/2307.07732/) (2023)
- [MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention](https://mlsanity.com/paper/2411.00472/) (2024)
- [A Comparative Study of YOLOv8 to YOLOv11 Performance in Underwater Vision Tasks](https://mlsanity.com/paper/2509.12682/) (2025)
- [Label-efficient underwater image classification with logistic regression on frozen foundation model embeddings](https://mlsanity.com/paper/2604.00313/) (2026)
- [Inference-Path Optimization via Circuit Duplication in Frozen Visual Transformers for Marine Species Classification](https://mlsanity.com/paper/2604.03428/) (2026)
- [How many labels do you need? A decision framework for cross-habitat marine species recognition](https://mlsanity.com/paper/2607.02559/) (2026)

## Abstract (arXiv)

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.