Abstract

Underwater object detection is important for ocean exploration and marine applications. However, underwater images are often degraded by absorption, scattering, and background interference, which weaken object contours, blur boundaries, and obscure fine texture details, thereby increasing the difficulty of detecting small objects and objects with large shape variations. To address these challenges, we propose WEC-UOD, an underwater object detector that improves structure-sensitive representation learning and multi-scale feature fusion within the detector, without relying on a separate image enhancement stage. In the backbone, the Wavelet–Edge Collaboration (WEC) module first uses wavelet-subband guidance to compensate for degraded structural and texture information and then applies edge-guided spatial correction to refine object boundaries and local geometry. In the neck, the Scale-Selective Fusion (SSF) module adaptively selects informative responses from branches with different receptive fields and further suppresses background interference through channel and spatial recalibration. Experiments on RUOD and DUO show that WEC-UOD achieves mAP@0.5 scores of 87.4% and 86.9%, respectively, consistently outperforming the YOLOv11s baseline. These results demonstrate the effectiveness of combining structural enhancement with selective multi-scale aggregation for underwater object detection.

IPC Classification

Keywords