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SPFDet: CLIP Text-Prior-Guided Structure-Enhanced Detector for Fine-Grained Ship Detection in Remote Sensing Images
1School of Data Science and Big Data Technology, Central South University, Changsha 410083, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
Ship detection in remote sensing images is enhanced by SPFDet, a novel detector using CLIP text-guided structure enhancement. This method improves accuracy in complex maritime environments, crucial for surveillance and security.
Area of Science:
- Computer Vision
- Remote Sensing
- Artificial Intelligence
Background:
- Ship detection is vital for maritime surveillance but challenged by complex backgrounds and ship similarities.
- Existing methods struggle with scale variations and fine-grained classification in remote sensing imagery.
Purpose of the Study:
- To develop an advanced ship detection system, SPFDet, that overcomes limitations of current detectors.
- To integrate vision-language models with structural feature enhancement for improved remote sensing ship identification.
Main Methods:
- Proposed SPFDet, a CLIP text-prior-guided structure-enhanced detector.
- Implemented a Semantic Prior Component (SPC) using CLIP text encoder for semantic embeddings.
- Introduced a Structure-Enhanced Transformer Module (SETM) with channel selection and cross-attention for feature enhancement.
- Developed a Fused Category-Dominated Feature (FCDF) generation and a Category-Semantic Consistency Loss.
Main Results:
- SPFDet achieved 97.2% mAP on the HRSC2016 benchmark.
- SPFDet achieved 72.8% mAP on the ShipRSImageNet benchmark.
- Demonstrated consistent improvements over existing strong detection baselines.
Conclusions:
- SPFDet effectively addresses challenges in remote sensing ship detection, including complex backgrounds and scale variations.
- The integration of vision-language priors and structure enhancement significantly boosts detection performance.
- SPFDet offers a promising solution for maritime surveillance and security applications.
