Due to wavelength dependent light absorption and scattering, underwater images usually suffer from color distortion and blurred details, which limits underwater object detection performance. Existing underwater image enhancement methods mainly focus on visual quality improvement, while it is still difficult to balance enhancement quality, processing efficiency, and downstream detection performance. Therefore, this paper proposes an efficient dual-branch underwater image enhancement framework for object detection. The detail enhancement branch improves brightness and local contrast to recover texture details in dark regions. The color restoration branch uses adaptive compensation to reduce color distortion and improve color gradation. By combining the complementary outputs of the two branches, the proposed framework provides clearer and more informative images for object detection. On the UIEB and EUVP datasets, the proposed method achieves UIQM scores of 2.249 and 2.576. When applied to the YOLOv8 detection task on the URPC dataset, the proposed method improves mAP50 by 2.1\% compared with the baseline. Extensive experiments show that our method improves object detection in complex underwater scenes, while balancing enhancement quality and processing efficiency.

翻译：由于波长依赖的光吸收和散射，水下图像通常存在颜色失真和细节模糊等问题，这限制了水下目标检测的性能。现有水下图像增强方法主要关注视觉质量的提升，但难以同时兼顾增强质量、处理效率与下游检测性能。为此，本文提出一种面向目标检测的高效双分支水下图像增强框架。细节增强分支通过提升亮度与局部对比度，恢复暗区纹理细节；色彩恢复分支采用自适应补偿以减少颜色失真并改善色调层次。通过融合两个分支的互补输出，该框架为目标检测提供更清晰、信息更丰富的图像。在UIEB和EUVP数据集上，所提方法分别取得了2.249和2.576的UIQM评分。将该方法应用于URPC数据集上的YOLOv8检测任务时，相较于基线方法，mAP50提升了2.1%。大量实验表明，所提方法能在复杂水下场景中提升目标检测性能，同时兼顾增强质量与处理效率。