YOLO-MSD: A Ship Target Detection Algorithm for Maritime Surveillance Scenarios

Ship detection in maritime monitoring is crucial for ensuring public safety in marine environments. However, maritime surveillance faces significant challenges due to weak targets (small, low-contrast objects) caused by complex environments and long distances. To address these challenges, we propose YOLO-MSD, a maritime surveillance detection model based on YOLOv8. In YOLO-MSD, Receptive-Field Attention Convolution (RFAConv) replaces standard convolution, learning attention maps via receptive-field interaction to enhance detail extraction and reduce information loss. The C2f module In the neck integrates Omni-Dimensional Dynamic Convolution (ODConv), which dynamically adjusts convolution kernel parameters to effectively capture contextual information, thereby achieving superior multi-scale feature fusion. We introduce a dedicated detection head specifically for small objects to enhance detection accuracy. Furthermore, to address detection box quality imbalance, we employ Wise-IoU for bounding box regression loss, enhancing multi-scale target localization and accelerating convergence. The model achieves precision, recall and mean average precision (mAP50) rates of 93.0%, 90.05% and 95.0%, respectively, on the self-constructed Maritime Vessel Surveillance Dataset (MVSD), effectively meeting the requirements for maritime target detection. We further conduct comparative experiments on the public McShips dataset, demonstrating YOLO-MSD's broad applicability in ship detection.