Abstract Underwater biological object detection is important for intelligent marine monitoring, yet its performance is often limited by severe image degradation, background clutter, and large variations in target scale. These factors can weaken feature representation during multi-scale fusion and reduce localization reliability in lightweight detectors. In this study, we propose UD-YOLO, a lightweight underwater object detector designed to improve cross-scale feature interaction and localization stability under degraded underwater conditions. The framework enhances contextual representation in the backbone, stabilizes bidirectional information propagation in the neck, and improves multi-scale localization with a lightweight shared detection head and scale-aware regression supervision. On the RUOD dataset, UD-YOLO improves mAP@0.5:0.95 by 2.1% points over YOLOv11n, while reducing parameter count by 0.1 M and computational cost by 0.2 GFLOPs. Additional evaluations on the URPC and DUO show consistent gains over YOLOv11n under different underwater benchmark settings. These results suggest that UD-YOLO provides an effective accuracy-efficiency trade-off for lightweight underwater biological object detection.
Zhu et al. (Sat,) studied this question.