Enhancing Marine Habitats Detection: A Comparative Study of Semi-Supervised Learning Methods

Rim Rahali, Thanh Phuong Nguyen, Vincent Nguyen

2025

Abstract

Most of the recent success in applying deep learning techniques to object detection relies on large amounts of carefully annotated and large training data, whereas annotating underwater images is a costly process and providing a large dataset is not always affordable. In this paper, we conduct a comprehensive analysis of multiple semi-supervised learning models, used for marine habitats detection, aiming to reduce the reliance on extensive labeled data while maintaining high accuracy in challenging underwater environments. Results, performed on Deepfish and UTDAC2020 datasets attest a significant performance conducted by semi-supervised learning, in terms of quantitative and qualitative evaluation. An other study related to Underwater Image Enhancement (UIE) methods and contrastive learning is presented in this work to deal with underwater images specificity and provide more comprehensive analysis of their impact on marine habitats detection.

Paper Citation

in Harvard Style

Rahali R., Nguyen T. and Nguyen V. (2025). Enhancing Marine Habitats Detection: A Comparative Study of Semi-Supervised Learning Methods. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 233-244. DOI: 10.5220/0013325500003912

in Bibtex Style

@conference{visapp25,
author={Rim Rahali and Thanh Nguyen and Vincent Nguyen},
title={Enhancing Marine Habitats Detection: A Comparative Study of Semi-Supervised Learning Methods},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={233-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013325500003912},
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 3: VISAPP
TI - Enhancing Marine Habitats Detection: A Comparative Study of Semi-Supervised Learning Methods
SN - 978-989-758-728-3
AU - Rahali R.
AU - Nguyen T.
AU - Nguyen V.
PY - 2025
SP - 233
EP - 244
DO - 10.5220/0013325500003912
PB - SciTePress