AMFF-DETR: anti-interference and multi-scale feature fusion DETR for SAR ship detection in complex backgrounds

Abstract

Ship detection in synthetic aperture radar images is difficult because ships vary greatly in scale, sea and land clutter can resemble targets, and speckle weakens object boundaries. This paper presents AMFF-DETR, an RT-DETR-based detector with four task-oriented modifications. MEIE-Net strengthens multi-scale contour features, dynamic histogram self-attention-based inter-scale feature interaction module groups features by intensity before attention, adaptive multi-branch cross-scale feature fusion adaptively fuses spatial and semantic information, and Inner-MPDIoU adds scaled-box overlap to minimum-point-distance regression. On SSDD, AMFF-DETR achieves 98.8% mAP 50 and 91.3% mAP 75; on HRSID, it achieves 93.4% and 78.1%, respectively. The model requires only 16.54 M parameters and 54.4 GFLOPs, while the loss-function and module-control experiments consistently demonstrate the effectiveness of the integrated design.