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Multi-Prior Fusion Transfer Plugin for Adapting In-Air Models to Underwater Image Enhancement and Detection.

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    This study introduces IA2U, a lightweight plugin for adapting in-air models to underwater environments without architectural changes. IA2U effectively enhances underwater image quality and object detection by integrating prior knowledge and aligning multi-scale features.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Underwater data scarcity and complex distributions challenge model training.
    • In-air models offer potential for transfer but suffer performance degradation in underwater settings due to visual discrepancies.

    Purpose of the Study:

    • To propose IA2U, a lightweight plugin for efficient underwater adaptation of existing in-air models.
    • To enable flexible integration into arbitrary networks with minimal cost and high generalizability.
    • To improve underwater image enhancement and object detection performance.

    Main Methods:

    • IA2U integrates prior knowledge (water type, degradation, semantics) via feature injection and channel-wise modulation.
    • A multi-scale feature alignment module balances information across resolution paths for enhanced consistency.
    • The plugin adapts models without altering their original architecture.

    Main Results:

    • IA2U significantly improves underwater image enhancement (e.g., +5.2 dB PSNR on UIEB dataset).
    • Object detection performance is enhanced (e.g., +1.8% AP on RUOD dataset with PAA detector).
    • IA2U demonstrates effectiveness in boosting Shallow-UWNet and PAA detector performance.

    Conclusions:

    • IA2U offers an effective and scalable solution for robust underwater perception systems.
    • The plugin achieves significant performance gains with minimal adaptation costs.
    • Code availability facilitates further research and application of IA2U.