Abstract
The growth in international trade has precipitated operational demands on port facilities, mandating the development of advanced intelligent monitoring systems. Existing ship detection algorithms struggle with feature confusion and difficulty in extracting contextual features under occlusion, which reduces the discriminability between object features and background noise. This leads to positional misalignment and mismatching of similar targets, which reduce the detection accuracy. To resolve this, we propose OShipNet, an architecture engineered to optimize feature fusion and refinement for occluded ship detection. First, we design the OShipNeXt backbone network, which provides complementary feature representation in frequency and spatial domains. This approach enables the reconstruction of global–local semantic associations for occluded objects, enhancing feature representation and improving detection accuracy. Secondly, to further refine target boundaries, we develop a Multiscale Pooling Attention Module (MSPAM) to enhance contextual awareness and better capture occluded edge features. Furthermore, we propose a dual-path cooperative loss function that mitigates the effects of low-quality bounding boxes. Comprehensive evaluations on the MVDD13 dataset demonstrate the robustness of OShipNet, which achieved 94.98% mAP@50 and 84.37% mAP@50-95, demonstrating advantages over existing object detection methods and establishing an effective framework for intelligent port monitoring.