A Novel Large-Swath Fast Ship Detection Framework with Adaptive Anchor Boxes

Synthetic aperture radar (SAR), capable of ultra-wide swath imaging, serves as a vital tool for ocean surveillance. Ship detection in SAR imagery holds significant importance for maritime security and traffic management. Current ship detection algorithms exhibit excessive computational complexity, and both conventional and deep learning-based approaches struggle to achieve rapid detection in ultra-wide swath SAR imagery acquired by next-generation SAR systems. This paper proposes a rapid adaptive anchor framework for ship detection. First, the detection criterion statistics are decomposed into multiple prefix sum matrices, ensuring minimal computational complexity for anchor boxes of arbitrary sizes. Second, the image is quadrant-divided, with detection criterion statistics computed separately to determine ship presence in each quadrant. Third, quadrants containing ships undergo iterative quadrant division, while ship-free quadrants are annotated using differential matrices. Finally, the initial anchor boxes are systematically shifted to repeat the second and third steps across the entire image, ultimately generating a sea clutter mask. The computational complexity of the proposed ship detection framework remains equivalent to that of loading the full SAR image, introducing negligible additional overhead to existing systems. The framework is validated using empirical SAR datasets, demonstrating enhanced computational efficiency without compromising detection accuracy.