# Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

arXiv: https://arxiv.org/abs/1805.10106 · HTML: https://mlsanity.com/paper/1805.10106/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

## Built on by (most cited first, 5 of 5)

- [Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey](https://mlsanity.com/paper/2203.06951/) (2022)
- [Deep learning with self-supervision and uncertainty regularization to count fish in underwater images](https://mlsanity.com/paper/2104.14964/) (2021)
- [Efficient Unsupervised Learning for Plankton Images](https://mlsanity.com/paper/2209.06726/) (2022)
- [FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation](https://mlsanity.com/paper/2403.10916/) (2024)
- [Lightweight Fish Classification Model for Sustainable Marine Management: Indonesian Case](https://mlsanity.com/paper/2401.02278/) (2024)

## Abstract (arXiv)

The target of this paper is to recommend a way for Automated classification of Fish species. A high accuracy fish classification is required for greater understanding of fish behavior in Ichthyology and by marine biologists. Maintaining a ledger of the number of fishes per species and marking the endangered species in large and small water bodies is required by concerned institutions. Majority of available methods focus on classification of fishes outside of water because underwater classification poses challenges such as background noises, distortion of images, the presence of other water bodies in images, image quality and occlusion. This method uses a novel technique based on Convolutional Neural Networks, Deep Learning and Image Processing to achieve an accuracy of 96.29%. This method ensures considerably discrimination accuracy improvements than the previously proposed methods.