SARLite: enhanced lightweight YOLO framework for SAR object detection

Object detection in Synthetic Aperture Radar (SAR) imagery remains a challenging task due to strong speckle noise, low semantic texture, and a significant domain gap from natural images. While recent approaches have addressed these challenges through complex pretraining strategies and transformer-based architectures, they often incur high computational costs, limiting their applicability in real-time and edge scenarios. In this work, we propose a lightweight SAR-specific detector built upon the YOLO11s framework. The proposed model integrates an enhanced HGNet backbone for stronger feature extraction, a lightweight structural design to reduce computational cost, and a dynamic upsampling method. On the two public datasets SARDet-100K and SSDD, the proposed method consistently outperforms existing approaches. Compared with the YOLO11-s baseline, our method achieves an average improvement of approximately 3.4% in mAP@50:95 and 0.9% in mAP@50, while significantly reducing model complexity, with about a 17% reduction in parameters and a 1 GFLOP decrease in computational cost. These results demonstrate that a streamlined architecture, carefully adapted to SAR characteristics, can achieve strong performance without relying on heavy pretraining or large-scale backbone networks.