SAR object detection via adaptive shift-wise downsampling and frequency-spatially modulated feature fusion

Object detection in synthetic aperture radar (SAR) imagery is challenged by speckle noise, background clutter, and large variations in target scale. We present ASCE-SAR, a YOLO11-based detector that coordinates adaptive shift-wise downsampling with frequency-spatially modulated multi-scale fusion. In the backbone, the Adaptive Downsampling with Shift-Wise Convolution (ADSC) block combines structure-preserving downsampling and enlarged receptive-field modeling. In the neck, Dynamic Shift-wise Feature Fusion (DSFF) uses content-aware resampling and frequency-spatial modulation to improve cross-scale feature alignment and weak-target representation. On the OGSOD-1.0 and HRSID test sets, ASCE-SAR achieves AP values of 59.9% and 67.9%, respectively, corresponding to gains of 7.1 and 1.8 percentage points over the YOLO11 Baseline under the same evaluation protocol. Under synthetic Gamma speckle perturbations on HRSID, the full model retains a 0.8–2.0 percentage-point mAP \(_{50{:}95}\) advantage over the Baseline across the clean condition and the evaluated noise levels. The full model contains 3.17 M parameters and requires 14.94 GFLOPS; its profiling results are reported with forward latency and peak memory to make the accuracy-cost trade-off explicit. These results indicate improved detection accuracy and noise-perturbed performance, accompanied by a measurable computational cost.