FSCR-Net: Fourier Series-Based Contour Refinement for Rotated Object Detection

Rotated object detection in remote sensing imagery is a core task of intelligent interpretation, used in urban management, resource surveys, and national defense. Remote sensing targets have large scale gaps, arbitrary orientations, and rotational symmetry, demanding robust feature extraction and shape description. Existing methods have two limitations: first, they cannot distinguish tiny targets’ valid high-frequency features from background clutter. Global frequency-domain filtering impairs large-object features, raising small-target miss rates. Second, rotated bounding boxes fail to depict irregular ground-object contours, and annotation starting points degrade training stability. To address these, we propose FSCR-Net, a frequency-sensitive contour reconstruction network. Leveraging Fourier series’ periodic symmetry for robust parametric contour representation, it improves detection and contour quality via three modules. First, a frequency-smoothness-aware feature fusion module decouples foreground/background features via soft masks and applies scale-adaptive frequency-domain filtering on background branches, suppressing clutter and enhancing tiny target discriminability without impairing large objects. Second, a differentiated contour representation strategy converts contours into parametric Fourier series, achieving unified dimensional normalization for multi-scale targets and compact shape encoding for detection regression. Third, a Fourier coefficient reconstruction detection head adopts a rolling optimization matching loss to eliminate annotation starting point interference and reconstructs contours via inverse Fourier transform. The proposed method achieves 76.52% mAP@0.5 on DOTA-v1.5, 92.15% mAP on HRSC2016, and 98.71% mAP on UCAS-AOD, outperforming state-of-the-art approaches in both detection accuracy and contour representation quality.