From Geometric Alignment to Scale Balance: Directional Strip Convolution and Efficient Scale Fusion for Remote Sensing Ship Detection
Jing Sun, Guoyou Shi, Yi Yang, Xiaolian Cheng · Remote Sensing · 2026
Optical remote sensing ship detection faces significant challenges in realistic maritime scenes due to strong background clutter (e.g., docks, shorelines, wake streaks), extreme scale variation, and the elongated geometry of ships with diverse orientations. These factors frequently lead to geometric misalignment, unstable localization, and false alarms, particularly in congested ports and complex sea states. To enhance robustness under clutter while retaining the set prediction efficiency of DETR, we propose the Directional Efficient Network (DENet), a structure-aware enhancement built upon RT-DETR. DENet introduces two complementary components. First, Directional Strip Convolution (DSConv) replaces the standard 3×3 convolution for spatial mixing. By predicting offsets conditioned on input features, DSConv performs strip aggregation that aligns with slender hull structures, thereby suppressing interference from line-shaped background patterns. Second, Efficient Scale Fusion (ESF) augments the Hybrid Encoder as an additive residual correction. It combines multiple receptive field branches with lightweight differential compensation to balance low-frequency context and high-frequency structural transitions, ensuring stable multi-scale fusion in cluttered scenes. Extensive experiments demonstrate the effectiveness of DENet. On ShipRSImageNet, APval improves from 58.8% to 63.2% and AP50val increases from 68.5% to 73.6%. Consistent gains are also observed on NWPU VHR-10, where APval reaches 63.0% and AP50val reaches 94.6%, alongside improvements on the Infrared Ship Database and VisDrone2019-DET, validating the method’s generalization capabilities.