Dynamic SAR image target detection by fusing space-frequency domain

A dynamic SAR image target detection algorithm integrating spatial-frequency domains is proposed to address several challenges inherent to SAR imagery, including significant feature variability, imbalanced target scales, and high speckle noise in background regions. These challenges contribute to decreased detection accuracy and slower inference speeds, posing difficulties for real-time applications. The proposed method is specifically designed to overcome these limitations through multiple innovative components that enhance both detection performance and computational efficiency. The algorithm first employs a dual-stream perception strategy to construct spatial-frequency perception units. This design integrates both dynamic receptive fields and fractional-order Gabor transforms, significantly improving the model’s ability to capture spatial diversity and frequency scattering features. By expanding the receptive fields adaptively, the algorithm captures both local and global contexts, leading to more effective extraction of complex patterns in the input data. Using fractional-order Gabor transforms further enhances the model's sensitivity to fine-grained texture and frequency features, which helps retain important global contextual information. These improvements collectively speed up inference by minimizing redundant feature representations, reducing the interference from background noise, and decreasing the similarity of feature mapping patterns. Consequently, the algorithm effectively addresses common issues such as missed and false detections, are typical in cluttered SAR images. In the next stage, a re-parameterization-based adaptive feature fusion module is introduced to optimize the interaction between multi-scale features. This module facilitates the efficient integration of features across different scales, enriching feature diversity and mitigating the discrepancies introduced during the sampling process. Additionally, the fusion process highlights the salience of small targets and key frequency information, often challenging to detect in traditional SAR detection frameworks. This enhanced multi-scale feature integration improves the detection accuracy, particularly for small and subtle objects, which are crucial in applications like maritime surveillance and remote sensing. To further enhance the algorithm’s effectiveness, a dynamic regression loss function, DY_IoU, is incorporated. This loss function employs adaptive scale penalty factors and a dynamic non-monotonic attention mechanism to address anchor box expansion and positional deviations. By dynamically adjusting the focus during training, the model achieves more precise localization of multi-scale targets. Moreover, the improved loss function facilitates faster convergence, reduces the computational burden, and ensures the algorithm remains lightweight and efficient for practical deployment. The proposed method was evaluated on two publicly available datasets, SAR-Acraft-1.0 and HRSID. Experimental results show that the algorithm achieves mAP@0.5 values of 95.9% and 98.8%, respectively, representing 5.2% and 1.2% improvements over baseline models. Additionally, the proposed approach outperforms other comparison algorithms, demonstrating its superiority. These results confirm that the algorithm not only enhances detection accuracy but also exhibits strong robustness and generalization capabilities, making it suitable for a wide range of real-world applications.