Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach

TL;DR

This paper presents a self-supervised learning method for underwater fish segmentation that eliminates the need for manual annotations, achieving high accuracy and efficiency across multiple challenging datasets.

Contribution

It introduces a novel self-supervised approach that learns robust fish segmentation without manual labels, outperforming existing methods and enabling real-time applications.

Findings

Achieves segmentation accuracy comparable to fully-supervised methods

Demonstrates strong generalization across unseen underwater datasets

Offers computational efficiency suitable for real-time deployment

Abstract

Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manual annotation impractical for many applications. This paper introduces a novel self-supervised learning approach for fish segmentation using Deep Learning. Our model, trained without manual annotation, learns robust and generalizable representations by aligning features across augmented views and enforcing spatial-temporal consistency. We demonstrate its effectiveness on three challenging underwater video datasets: DeepFish, Seagrass, and YouTube-VOS, surpassing existing self-supervised methods and achieving segmentation accuracy comparable to fully-supervised methods without the need for costly annotations. Trained on DeepFish, our model exhibits strong generalization, achieving high segmentation accuracy on the unseen Seagrass and YouTube-VOS datasets. Furthermore, our model is computationally efficient due to its parallel processing and efficient anchor sampling technique, making it suitable for real-time applications and potential deployment on edge devices. We present quantitative results using Jaccard Index and Dice coefficient, as well as qualitative comparisons, showcasing the accuracy, robustness, and efficiency of our approach for advancing underwater video analysis