MSFE-YOLO: Real-Time Underwater Object Detection via Multi-Scale Feature Aggregation and Frequency-Domain Enhancement

Underwater target detection has become a prominent research area in computer vision, while high noise levels, low visibility, and blurred edges pose significant challenges, which severely degrade the performance of existing detection algorithms. Such challenges necessitate detection models that simultaneously achieve high accuracy, operational stability, superior generalization, real-time inference, and architectural efficiency. To address these challenges, we propose the multi-scale feature enhancement YOLO (MSFE-YOLO), which integrates four key improvements: First, the CSP-PMSFA module is proposed to preserve multi-scale information and enhance feature fusion. Second, the C2PSA-EDFFN module is designed to effectively capture frequency-domain information and provide complementary local details. Third, the DetectAux module is incorporated to address feature learning inconsistencies in multi-scale detection through deep supervision and hierarchical label assignment. Finally, the improved D-InterpIoU loss is introduced as a novel objective function that benefits the training stages. On the URPC dataset, MSFE-YOLO achieves mAP50 and mAP50:95 of 86.0% and 50.9%, surpassing YOLOv11n by 1.7% and 1.4%, respectively. On the UTDAC dataset, MSFE-YOLO achieves mAP50 and mAP50:95 of 84.3% and 50.2%, exceeding YOLOv11n by 1.2% and 0.9%, respectively, while maintaining a real-time inference speed of over 300 FPS on an NVIDIA RTX 3090 Ti. These results validate that MSFE-YOLO achieves an effective balance between accuracy and efficiency for underwater object detection, with notable advantages in detecting small-scale targets.