Enhancing underwater object detection through bidirectional cross-scale gated feature fusion

Abstract

Underwater target detection is hindered by light scattering, low contrast, blur, occlusion, and background clutter, which particularly affect small objects. To address these limitations, we propose RCG-YOLO, a YOLOv8n-based detector that improves feature preservation, multi-scale feature extraction, and cross-scale feature fusion. Specifically, the proposed model introduces a residual down-sampling (ResDown) module to preserve fine spatial information during downsampling, a gated attention and multi-scale extractor (GAME) module to strengthen multi-scale feature extraction and positional sensitivity, and a cross-scale gated fusion (CGF) module to selectively fuse shallow spatial features and deep semantic features. Experiments on three public underwater datasets shows consistent improvements over YOLOv8n. RCG-YOLO achieves mAP50 scores of 85.2 ± 0.03%, 86.5 ± 0.02%, and 85.0 ± 0.03% on UTDAC2020, DUO, and RUOD datasets, corresponding to absolute mAP50 gains of 3.4%, 3.9%, and 1.7% over the baseline, respectively. Ablation studies confirm that ResDown, GAME, and CGF each contribute to the final detection performance. Although RCG-YOLO maintains real-time inference on an NVIDIA RTX 4090, its higher GFLOPs and lower frames per second compared with YOLOv8n indicate that the method prioritizes accuracy over maximum inference efficiency. These results indicate that RCG-YOLO provides a practical accuracy improvement over YOLOv8n for underwater object detection.