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.
Rahali et al. (Thu,) studied this question.