提出双分支协同增强框架，提升水下图像检测效果
A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection
- 双分支设计：细节增强与色彩恢复并行优化
- 在UIEB和EUVP上分别达2.249和2.576的UIQM分数
- 应用于YOLOv8使mAP50提升2.1%，适合水下视觉任务
由于波长相关的光吸收和散射，水下图像常出现颜色失真和细节模糊，限制了目标检测性能。现有水下图像增强方法主要关注视觉质量提升，但在增强质量、处理效率与下游检测性能之间仍难平衡。为此，本文提出一种高效双分支水下图像增强框架，用于目标检测。细节增强分支提升亮度与局部对比度，恢复暗区纹理；色彩恢复分支采用自适应补偿减少颜色失真，改善色阶。通过融合两分支互补输出，该框架为检测提供更清晰、信息量更高的图像。在UIEB和EUVP数据集上，该方法分别获得2.249和2.576的UIQM分数；在URPC数据集上应用于YOLOv8检测任务时，相比基线模型，mAP50提升2.1%。大量实验表明，该方法在复杂水下场景中有效提升目标检测性能，同时兼顾增强质量与处理效率。
原文摘要 · Abstract (English)
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.
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