A dynamic SAR image target detection algorithm integrating space-frequency domains is proposed to address challenges such as significant feature differences in SAR image samples, imbalanced target scales, and high speckle noise in the background, which result in low detection accuracy and slow inference speed. First, a dual-stream perception strategy constructs spatial-frequency perception units, leveraging dynamic receptive fields and fractional-order Gabor transforms to enhance the model’s ability to capture spatial diversity and frequency scattering features. This way improves the retention of global contextual information, accelerates inference, reduces the similarity of feature mapping patterns, and mitigates background noise interference, effectively reducing missed and false detections. Second, a re-parameterization-based adaptive feature fusion module is designed to optimize interactions across multi-scale features, enriching feature diversity, alleviating mapping discrepancies and information loss caused by feature sampling, and enhancing the salience of small target and key frequency information during fusion, thereby improving detection precision. Finally, the DY_IoU dynamic regression loss function is introduced, utilizing adaptive scale penalty factors and a dynamic non-monotonic attention mechanism to address anchor box expansion and positional deviation, further enhancing the localization and detection capabilities for multi-scale targets. This way also accelerates model convergence and reduces computational overhead. Experiments conducted on the public datasets SAR-Acraft-1.0 and HRSID demonstrate that the proposed method achieves mAP@0.5 values of 95.9% and 98.8%, respectively, representing 5.2% and 1.2% improvements over baseline models and outperforming other comparison algorithms. These results indicate that the proposed algorithm improves detection accuracy and exhibits strong robustness and generalization capabilities.