MPFT-UNet: A Boundary-Refined and Multi-Scale Dynamic Fusion Network for UAV-Based Port Ship Segmentation

Ship semantic segmentation based on unmanned aerial vehicle (UAV) imagery has important application value in maritime scenarios such as marine surveillance, port management, and maritime safety. However, UAV images often contain large scale variations of ships, a high proportion of small targets, and complex background interference, including sea surface reflections, waves, and clouds. These factors make accurate segmentation and boundary localization difficult. To address these issues, this paper proposes a UAV-based ship semantic segmentation network, termed MPFT-UNet. The network introduces a Multi-scale Dynamic Sparse Cross-gating (MDSC) module to improve the representation of small targets. A Boundary Supervision Refinement (BSR) module is used to enhance boundary delineation. In addition, a Transformer-based Feature Fusion (FFT) module is applied at the bottleneck layer to strengthen global semantic representation. Experimental results show that MPFT-UNet achieves better performance than existing methods across multiple evaluation metrics. The model obtains an IoU of 0.8365, Dice coefficient of 0.9028, Recall of 0.8881, and AP of 0.95731. These results indicate stable segmentation performance under complex maritime conditions. Compared with the baseline U-Net model, the IoU is improved by approximately 5.1%.