Domain-Unified Adaptive Detection Framework for Small Vehicle Targets in Monostatic/Bistatic SAR Images

Benefiting from the advantages of unmanned aerial vehicle (UAV) platforms such as low cost, rapid deployment capability, and miniaturization, the application of UAV-borne synthetic aperture radar (SAR) has developed rapidly. Utilizing a self-developed monostatic Miniaturized SAR (MiniSAR) system and a bistatic MiniSAR system, our team conducted multiple imaging missions over the same vehicle equipment display area at different times. However, system disparities and time-varying factors lead to a mismatch between the distributions of the training and test data. Additionally, small ground vehicle targets under complex background clutter exhibit limited size and weak scattering characteristics. These two issues pose significant challenges to the precise detection of small ground vehicle targets. To address these issues, this article proposes a domain-unified adaptive target detection framework (DUA-TDF). The approach consists of two stages: image-to-image translation and feature extraction and target detection. In the first stage, a multi-scale detail-aware CycleGAN (MSDA-CycleGAN) is proposed to align the source and target domains at the image level by achieving unpaired image style transfer while emphasizing both global structure and local details of the generated images. In the second stage, a cross-window axial self-attention target detection network (CWASA-Net) is proposed. This network employs a hybrid backbone centered on the cross-window axial self-attention mechanism to enhance feature representation, coupled with a convolution-based stacked cross-scale feature fusion network to strengthen multi-scale feature interaction. To validate the effectiveness and generalization capability of the proposed algorithm, comprehensive experiments are conducted on both self-developed monostatic/bistatic SAR datasets and public dataset. Experimental results demonstrate that our method achieves an mAP50 exceeding 90% in within-domain tests and maintains over 80% in cross-domain scenarios, demonstrating exceptional and robust detection performance as well as cross-domain adaptability.