Ship-DiffDet: A Lightweight Diffusion Model for Small-Object Ship Detection

Ship detection over long distances is crucial for the visual perception of intelligent ships. AI techniques, particularly machine learning and deep learning, have achieved a series of breakthroughs in this field. However, due to the limited pixels of ships over long distances, such objects often suffer from weak feature representation and are susceptible to interference in complex environments. To address these challenges, this paper proposes an improved architecture named Ship-DiffDet, based on DiffusionDet. First, we redesign the backbone feature extraction network and propose IDC-Net, which utilizes inception depthwise convolution to enhance feature extraction efficiency while reducing computational complexity. Second, to tackle the difficulty of effectively extracting features from small objects, we design a Hybrid Pooling Attention-enhanced Feature Pyramid Network. By incorporating a hybrid pooling attention mechanism, it strengthens multi-scale feature fusion, thereby improving the performance of the detection heads. Furthermore, we introduce a multi-order gated aggregation mechanism into the dynamic detection head to optimize dynamic convolution and further compress the model’s parameter count. Experimental results demonstrate our method achieves an effective balance between detection accuracy and computational efficiency. On our custom-built small-object ship dataset, the proposed method improves AP50 by 1.7% over the baseline while reducing the parameter and FLOPs counts by 48.8% and 22%, respectively.