Improved SAR Aircraft Detection Algorithm Based on Visual State Space Models

In recent years, the development of deep learning algorithms has significantly advanced the application of synthetic aperture radar (SAR) aircraft detection in remote sensing and military fields. However, existing methods face a dual dilemma: CNN-based models suffer from insufficient detection accuracy due to limitations in local receptive fields, whereas Transformer-based models improve accuracy by leveraging attention mechanisms but incur significant computational overhead due to their quadratic complexity. This imbalance between accuracy and efficiency severely limits the development of SAR aircraft detection. To address this problem, this paper propose a novel neural network based on state space models (SSM), termed the Mamba SAR detection network (MSAD). Specifically, we design a feature encoding module, MEBlock, that integrates CNN with SSM to enhance global feature modelling capabilities. Meanwhile, the linear computational complexity brought by SSM is superior to that of Transformer architectures, achieving a reduction in computational overhead. Additionally, we propose a context-aware feature fusion module (CAFF) that combines attention mechanisms to achieve adaptive fusion of multi-scale features. Lastly, a lightweight parameter-shared detection head (PSHead) is utilised to effectively reduce redundant parameters through implicit feature interaction. Experiments on the SAR-AirCraft-v1.0 and SADD datasets show that MSAD achieves higher accuracy than existing algorithms, whereas its GFLOPs are 2.7 times smaller than those of the Transformer architecture RT-DETR. These results validate the core role of SSM as an accuracy-efficiency balancer, reflecting MSAD's perceptual capability and performance in SAR aircraft detection in complex environments.