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

2026-06-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

A new dual-branch collaborative framework has been developed to jointly optimize underwater image enhancement and object detection, addressing challenges like color distortion and blurred details caused by light absorption and scattering. This framework features a detail enhancement branch that improves brightness and local contrast to recover texture in dark regions, alongside a color restoration branch using adaptive compensation to reduce color distortion and improve color gradation. By combining their complementary outputs, the system provides clearer, more informative images for downstream object detection tasks. The method achieved UIQM scores of 2.249 on the UIEB dataset and 2.576 on the EUVP dataset. Furthermore, when applied to YOLOv8 detection on the URPC dataset, it improved mAP50 by 2.1% over the baseline, demonstrating enhanced object detection in complex underwater scenes while balancing enhancement quality and processing efficiency.

Key takeaway

For Computer Vision Engineers developing underwater object detection systems, this framework offers a robust approach to overcome common image degradation. You should consider integrating a dual-branch enhancement strategy, focusing on separate detail and color restoration, to significantly boost detection accuracy. This method improved mAP50 by 2.1% on the URPC dataset, suggesting a direct path to better performance in challenging underwater scenes.

Key insights

A dual-branch framework effectively enhances underwater images for improved object detection by separating detail and color restoration.

Principles

• Underwater image quality impacts detection.
• Dual-branch processing improves clarity.
• Balance enhancement, efficiency, detection.

Method

The framework uses a detail enhancement branch for brightness and contrast, and a color restoration branch with adaptive compensation, combining their outputs for clearer images.

In practice

• Apply dual-branch enhancement pre-detection.
• Evaluate UIQM alongside mAP50 for underwater tasks.
• Consider adaptive compensation for color.

Topics

• Underwater Image Enhancement
• Object Detection
• Dual-Branch Framework
• YOLOv8
• Computer Vision
• Image Quality Metrics

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.