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Object detection oriented underwater image enhancement based on gradient-guided generator and grouped spatial

Qiuyue Wang, Fen Chen, Zongju Peng

    Applied Optics
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    This study introduces a new underwater image enhancement method using a gradient-guided generator and grouped spatial attention (GSA) to improve object detection accuracy. The method significantly boosts performance on underwater datasets, outperforming existing techniques.

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    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Underwater image distortion from absorption and scattering degrades object detection accuracy.
    • Existing methods often prioritize visual perception over machine vision task performance.

    Purpose of the Study:

    • To develop an object detection-oriented underwater image enhancement technique.
    • To improve the accuracy and performance of machine vision tasks in underwater environments.

    Main Methods:

    • Proposed a gradient-guided generator network for detailed underwater image representation.
    • Introduced grouped spatial attention (GSA) to aggregate spatial context and enhance feature representation.
    • Evaluated the method on multiple underwater image datasets, including ChinaMM.

    Main Results:

    • Achieved 98.3% on one metric and 78.7% on Average Precision (AP) on the ChinaMM dataset.
    • Demonstrated significant improvements in object detection accuracy compared to other methods.
    • Showcased superior overall performance in underwater object detection tasks.

    Conclusions:

    • The proposed gradient-guided generator and GSA method effectively enhances underwater images for object detection.
    • The technique significantly improves machine vision task accuracy in challenging underwater conditions.
    • This approach offers a promising solution for reliable underwater object detection.