Overcoming annotation bottlenecks in underwater fish segmentation: a robust self-supervised learning approach
Saleh, Alzayat, Sheaves, Marcus, Jerry, Dean, and Azghadi, Mostafa Rahimi (2025) Overcoming annotation bottlenecks in underwater fish segmentation: a robust self-supervised learning approach. Signal, Image and Video Processing, 19 (4). 270.
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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.
| Item ID: | 88148 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1863-1711 |
| Keywords: | Computer vision, Convolutional neural networks, Deep learning, Self-supervised learning, Transformer, Underwater videos |
| Copyright Information: | ©TheAuthor(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 26 Mar 2026 06:29 |
| FoR Codes: | 31 BIOLOGICAL SCIENCES > 3103 Ecology > 310305 Marine and estuarine ecology (incl. marine ichthyology) @ 100% |
| SEO Codes: | 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180502 Assessment and management of pelagic marine ecosystems @ 100% |
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