Multi-Dimensional Collaborative Enhancement Method for Small-Object Detection in Complex Underwater Scenarios
TL;DR
Results support the effectiveness of the proposed framework for mixed-scale underwater object detection, including small-scale objects, and compare with the YOLOv11 baseline.
Abstract
Underwater object detection is challenged by scale variation, light attenuation, scattering, and non-uniform illumination, which weaken texture and boundary cues and increase background interference, particularly for small and distant objects. To address these issues, this study proposes a multi-dimensional collaborative enhancement framework. A Feature Enhancement Module (FEM) integrates local and contextual information through heterogeneous convolutional branches and residual learning. A Spatial-Attention-Based Spatial Pyramid Pooling (SPP_SA) module combines multi-scale pooling with spatial attention to suppress irrelevant background responses. A Soft Nearest-Neighbor Interpolation (SNI) module introduces response-scaled nearest-neighbor upsampling to regulate feature magnitude during resolution recovery and support cross-level fusion. Experiments on UTDAC2020, Brackish, and RUOD demonstrate consistent improvements under different underwater conditions. Compared with the YOLOv11 baseline, the proposed method improves mAP50:95 by 5.56%, 3.37%, and 0.81% on UTDAC2020, Brackish, and RUOD, respectively. These results support the effectiveness of the proposed framework for mixed-scale underwater object detection, including small-scale objects.