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Improving rare-class detection in deep-sea imagery via generative augmentation with stable diffusion

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Abstract

Megabenthos play a critical role in maintaining deep-sea ecosystem stability, making accurate detection important for deep-sea conservation. However, the high cost of deep-sea exploration and the long-tailed distribution of available datasets lead to severe data scarcity for rare species, limiting deep-sea benthos detection. To address this challenge, we propose a data augmentation framework based on Stable Diffusion (SD) and ControlNet. Specifically, we fine-tune a pretrained SD model using Low-Rank Adaptation (LoRA) to synthesize images of rare benthos, and leverage ControlNet to composite the generated targets into deep-sea backgrounds with controllable layouts and automatic bounding-box annotation. We constructed two megabenthos datasets collected using an optically tethered underwater vehicle (OTV) and an autonomous underwater vehicle (AUV), covering 16 biological categories; data augmentation was applied to 7 rare species with the fewest samples. The generated images achieved a Fréchet Inception Distance (FID) of 117.11 and an Inception Score (IS) of 4.97. When combined with real data for RT-DETR training, the augmentation strategy increased the AP50-95 and AP50 on the OTV dataset to 45.2% and 75.2%, representing improvements of 3.7% and 6.1% over the baseline. Similarly, on the AUV dataset, it increased the AP50-95 and AP50 to 36.8% and 64.7%, yielding enhancements of 2.2% and 4.2% over the baseline. Gains were especially pronounced for tail classes, with AP50-95 increased by 23.6% and 21.9% for Octopus and Bryozoa on the OTV dataset, and by 15.1% and 14.6% for Bryozoa and Hydrozoa on the AUV dataset. Moreover, the proposed approach outperforms traditional augmented methods by 1.6% and 0.8% in AP50-95 on the OTV and AUV datasets, respectively, indicating its utility for improving detection in deep-sea megabenthic surveys.

Data availability

The source code and trained models weights are publicly available on GitHub at https://github.com/dengjunlan/deepsea-benthos-SD-Augmentation. The raw datasets and metadata supporting this study are available from the corresponding author upon reasonable request, subject to the data protection regulations of the Beijing Pioneer Hi-Tech Development Corporation.

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Acknowledgements

This work was supported by the High-performance Computing Platform of China University of Geosciences Beijing.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No.52394252), and the National Key R&D Program of China (Grant No.2022YFC2804003 and Grant No.2023YFC2811405-4).

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Authors and Affiliations

  1. State Key Laboratory of Deep Earth Exploration and Imaging, China University of Geosciences (Beijing), Beijing, 100083, China

    Junlan Deng, Dingbang Wei & Jianxin Xia

  2. Key Laboratory of Polar Geology and Marine Mineral Resources, Ministry of Education, Beijing, 100083, China

    Junlan Deng, Dingbang Wei & Jianxin Xia

  3. School of Ocean Sciences, China University of Geosciences (Beijing), Beijing, 100083, China

    Junlan Deng, Dingbang Wei & Jianxin Xia

  4. Department of Digital Ocean, Beijing Pioneer Hi-Tech Development Corporation, Beijing, 100081, China

    Mi Duan

  5. School of Artificial Intelligence, China University of Geosciences (Beijing), Beijing, 100083, China

    Wei Song

  6. Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361000, China

    Xuebao He

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  1. Junlan Deng
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  2. Mi Duan
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  3. Dingbang Wei
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  4. Wei Song
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  5. Xuebao He
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  6. Jianxin Xia
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Contributions

J.D. conducted the literature search and conceived the experiments. M.D. collected the data and performed statistical analysis. D.W. prepared the figures. X.H. validated the dataset annotations. W.S. and J.X. led the preparation of the final version and provided critical feedback. All authors reviewed the manuscript.

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Jianxin Xia.

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Deng, J., Duan, M., Wei, D. et al. Improving rare-class detection in deep-sea imagery via generative augmentation with stable diffusion.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-45732-6

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