Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning

TL;DR

This paper presents a novel deep learning approach using CNNs for underwater fish species classification, achieving high accuracy and addressing challenges unique to underwater imaging.

Contribution

It introduces a new CNN-based method tailored for underwater fish classification, improving accuracy over previous approaches.

Findings

Achieved 96.29% classification accuracy.

Addresses underwater image challenges like noise and distortion.

Outperforms previous methods in accuracy.

Abstract

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.