論文ID: 2024EDP7316
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.