Work

Beyond labels: Semi-supervised underwater habitat detection with image enhancement as augmentation

Year: 2026

Type: article

Abstract: Recent progress in deep learning for object detection has largely relied on big, well-labeled datasets. But when it comes to underwater imagery, annotation becomes a real challenge. It typically requires domain experts and a lot of manual effort. As a result, building large-scale labeled datasets in this field is not only time-consuming but also often impractical. In this paper, we present an in-depth investigation of semi-supervised learning models for marine habitat detection, with the goal of reducing the need for large amounts of labeled data while maintaining strong performance in the complex conditions found underwater. We evaluate these models using the Deepfish and UTDAC2020 datasets. By extensive experimentation, we explore how various factors influence performance: the amount of labeled training data, the role of contrastive learning, and the application of underwater image enhancement as an augmentation technique. Overall, this work provides a detailed analysis and highlights the potential of these techniques in marine habitat detection.

Source: Multimedia Tools and Applications

DOI: 10.1007/s11042-026-21439-8

Authors: Rim Rahali, Vincent Nguyen

Institution: Université d'Orléans

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: hybrid

Estimated APC paid: $3,190