A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection

2026-06-14

AI Summary

Underwater images suffer from color distortion and detail blurring due to light absorption and scattering, which severely degrades object detection performance. To address this, this work proposes a dual-branch collaborative enhancement framework: a detail enhancement branch improves brightness and local contrast in dark regions, while a color restoration branch reduces color casts through adaptive compensation. The decoupled design of the two branches balances visual quality with the requirements of downstream tasks. Integrated with YOLOv8, the method enables end-to-end optimization, achieving UIQM scores of 2.249 and 2.576 on the UIEB and EUVP datasets, respectively, and improving YOLOv8’s mAP50 by 2.1% on the URPC dataset, thereby significantly enhancing detection accuracy in complex underwater scenes.

Abstract

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.

Problem

• underwater image enhancement
• object detection
• color distortion
• detail blurring
• performance balance

Innovation

• dual-branch framework
• underwater image enhancement
• object detection
• color restoration
• detail enhancement