DCP-UDet: Detail-Preserving Context Propagation for Underwater Object Detection

Small underwater objects are often distinguished by weak contours and textures that multiscale aggregation can suppress. We propose DCP-UDet to preserve these local features as contextual information passes through the detector. Spatially embedded cross-channel attention extracts shared context, and wavelet–context refinement modulates local responses using directional detail. Gated scale fusion then controls the coarse features added to a fine-resolution residual path. During training, normalized scale-and-visibility weighting emphasizes difficult targets, while paired-view consistency regularizes predictions under photometric degradation. On the study-specific DUO split, DCP-UDet achieves 65.2% AP and 44.6% small-object AP, exceeding DEIM-D-FINE-S by 1.4 and 2.4 percentage points, respectively. The model has 14.8 million parameters and requires 36.2 GFLOPs, with a batch-one latency of 8.4 ms on an RTX A6000. Component and objective ablations show gains from both selective propagation and difficulty-normalized supervision. Under severe image corruption, the detector retains 79.8% of its clean-image AP. The results support refining coarse context before selectively adding it to fine features for underwater detection.