Joint Image Enhancement for Underwater Object Detection in Various Domains

Underwater environments present significant challenges, such as image degradation and domain discrepancies, that severely impact object detection performance. Traditional approaches often use image enhancement as a preprocessing step, but this adds computational overhead, latency, and can even degrade detection accuracy. To address these issues, we propose a novel underwater object detection framework that jointly trains image enhancement within a multitask architecture. This framework employs a progressive training strategy to iteratively improve detection performance through enhancement and introduces a domain-adaptation mechanism to align features across domains at both image and object levels. Experimental results demonstrate that our method achieves state-of-the-art performance across diverse data sets, with real-time detection at 105.93 frames per second and over +15$\%$mean average precision absolute improvement in unseen environments, underscoring its potential for real-world underwater applications.