Strip Convolution and Direction-Aware Exclusion Loss for Oriented Ship Detection

The paper introduces a new oriented ship detector that combines a C3k2_Strip module, which uses orthogonal strip convolutions to better capture elongated hull structures, with a Class-Aware Direction-Aware Exclusion Loss (CA-DAEL) that suppresses redundant predictions by leveraging class, direction, and confidence cues. Experiments on HRSC2016 and DIOR-R datasets show the method achieving 78.45% and 53.71% mAP50:95, respectively, with only 2.91M parameters. On HRSC2016, the approach outperforms the YOLOv11-OBB baseline by 6.32 percentage points in mAP50:95, highlighting its effectiveness for accurate oriented ship detection.

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