FM-DETR: A Frequency–Morphology Collaborative Transformer for Ship Detection in the SAR Range-Compressed Domain

Ship detection in range-compressed (RC) synthetic aperture radar (SAR) images requires recognizing spatially extended responses rather than compact target appearances. Because elongated responses are not unique to ships, clutter can interfere with both feature aggregation and candidate selection. We propose FM-DETR, a frequency–morphology collaborative Transformer that extends RC-aware feature modeling to decoder-query initialization. An asymmetric cross-scale feature pyramid uses orthogonal strip convolutions to accommodate the unequal range and azimuth extents of target responses. A Fourier-gated intra-scale interaction module complements spatial self-attention with learnable spectral modulation, while a content-dependent gate regulates the spectral residual. The refined representation provides spatial guidance for multiscale candidate scoring and query selection. Evaluation follows the official RCShip-1.0 split, with synthetic training samples and scene-derived test samples reconstructed in the RC domain. FM-DETR achieves average precision (AP@0.5) of 90.14%, a precision of 89.53%, and a recall of 84.42%. With the backbone and training settings held fixed, the complete model improves AP@0.5 by 4.00 percentage points over the baseline. These results support integrating direction-sensitive aggregation, spectral refinement, and guided query initialization for ship detection in RC images.