UAED-Net: a unified adaptive enhancement and detection network with multi-scale feature refinement for underwater scenarios

Abstract Underwater object detection (UOD) is pivotal for applications in aquaculture, marine resource exploration, and environmental monitoring. However, relying solely on vision-guided image enhancement techniques as a preprocessing step for UOD is inadequate to address the prevalent degradation challenges in underwater imaging. To overcome the limitation, this paper proposes an unified adaptive enhancement and detection network (UAED-Net), which enhances the texture information of detection features through progressively integrating enriched features generated by an enhancement module; thereby improving the overall performance of the detector. Specifically, UAED-Net incorporates a 2nd-order Sobel operator within the detection-aware feature enhancement module. The operator’s elevated central weights enhance its sensitivity to subtle texture variations and structurally complex edges and corners in images. By processing images across horizontal, vertical, and diagonal directions, it enables the extraction of comprehensive texture features. Joint training of the enhancement module and the UOD network provides auxiliary discriminative information, further strengthening the network’s predictive capabilities. To achieve effective integration of enhanced and detection features, as well as cross-scale feature fusion across different dimensions, a mutual adaptive feature fusion model is introduced. This model enhances the spatial representation of object features, enabling the detection branch to learn richer target information and optimize detection performance. Experimental results on four challenging UOD datasets demonstrate that the proposed UAED-Net achieves superior performance, highlighting its effectiveness in addressing the complexities of underwater imaging. Link to open-source code: https://github.com/LeeBincheng/UAED-Net.