CEH-DETR: A State Space-Based Framework for Efficient Multi-Scale Ship Detection

Ship detection in optical images is critical for maritime supervision but faces challenges from scale variations and complex backgrounds. Existing detectors often struggle to balance global context modeling with computational efficiency. To address this, we propose Contextual Efficient Hierarchical DETR (CEH-DETR), an efficient framework for multi-scale ship detection. First, we introduce the Cross-stage Parallel State Space Hidden Mixer (CPSHM) backbone, integrating State Space Models with CNNs to capture global dependencies with linear complexity. Second, the Efficient Adaptive Feature Integration (EAFI) module reduces attention complexity to linear using Token Statistics-based Attention. Third, the Hierarchical Attention-guided Feature Pyramid Network (HAFPN) effectively fuses multi-scale features while preserving spatial details. Experiments on the ABOships dataset demonstrate that CEH-DETR achieves a superior balance between accuracy and efficiency. Relative to the baseline RT-DETR, our approach achieves a parameter reduction of 25.6% while increasing mAP@50 by 2.0 percentage points and boosting inference speed to 133.7 FPS (+112.1%), making it highly suitable for real-time maritime surveillance.