A Computer Vision Approach to Counting Farmed Fish in Flowing Water

Masanori Nishiguchi, Hitoshi Habe, Hitoshi Habe, Koji Abe, Koji Abe, Masayuki Otani, Masayuki Otani, Nobukazu Iguchi, Nobukazu Iguchi

2025

Abstract

Aquaculture is an expanding industry that depends on accurate fish counting for effective production management, including growth monitoring and feed optimization. Manual counting is time-consuming and labor-intensive, while commercial counting devices face challenges such as high costs and space constraints. In ecology, tracking animal movement trajectories is essential, but using devices on small organisms is impractical, prompting the adoption of video and machine learning techniques. In contrast to traditional biological studies that often rely on offline analysis, real-time fish counting is vital in aquaculture. This study introduces a fish count method based on a Multiple Object Tracking (MOT) algorithm explicitly tailored for aquaculture. The method prioritizes counting accuracy over precise movement tracking, optimizing existing techniques. The proposed approach provides a viable solution to count fish in aquaculture and potentially other fields.

Paper Citation

in Harvard Style

Nishiguchi M., Habe H., Abe K., Otani M. and Iguchi N. (2025). A Computer Vision Approach to Counting Farmed Fish in Flowing Water. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 781-789. DOI: 10.5220/0013388800003912

in Bibtex Style

@conference{visapp25,
author={Masanori Nishiguchi and Hitoshi Habe and Koji Abe and Masayuki Otani and Nobukazu Iguchi},
title={A Computer Vision Approach to Counting Farmed Fish in Flowing Water},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={781-789},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013388800003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - A Computer Vision Approach to Counting Farmed Fish in Flowing Water
SN - 978-989-758-728-3
AU - Nishiguchi M.
AU - Habe H.
AU - Abe K.
AU - Otani M.
AU - Iguchi N.
PY - 2025
SP - 781
EP - 789
DO - 10.5220/0013388800003912
PB - SciTePress