SPFDet: CLIP Text-Prior-Guided Structure-Enhanced Detector for Fine-Grained Ship Detection in Remote Sensing Images

Ship detection in remote sensing images is crucial for maritime surveillance, port management, and national security. However, existing detectors struggle with complex harbor backgrounds, large-scale variations, and fine-grained inter-class similarities among diverse ship categories. In this paper, we propose SPFDet, a CLIP text-prior-guided structure-enhanced detector that systematically addresses these challenges by integrating vision-language semantic priors with channel-selective feature enhancement. First, a Semantic Prior Component (SPC) employs a frozen CLIP text encoder to generate category-level semantic embeddings from textual descriptions of ship types, which are combined with visual scene priors and detail-aware priors to provide hierarchical domain-specific guidance. Second, a Structure-Enhanced Transformer Module (SETM), equipped with a Cross-level Channel Selection Module (CCSM) and Multi-Head Cross-Attention (MHCA), selectively enhances discriminative channel responses associated with hull contours, deck textures, and fine structural patterns across encoder layers. Third, a Fused Category-Dominated Feature (FCDF) generation mechanism integrates CLIP-based semantic priors with multi-scale visual features through category-guided attention, producing category-aware representations for precise classification and localization. We further introduce a Category-Semantic Consistency Loss to enforce alignment between predicted features and CLIP text priors. Extensive experiments on HRSC2016 and ShipRSImageNet benchmarks demonstrate that SPFDet achieves 97.2% and 72.8% mAP respectively, providing consistent improvements over strong detection baselines while maintaining competitive inference speed.