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To address this, we propose an improved YOLO11-based model specifically designed for detecting dense and rotated small objects in UAV scenarios. To better extract features from rotated targets, we design a C3k2\u2013ARC module that enhances the model\u2019s rotational detection capability. In addition, we introduce the CL-Concat feature fusion module, which combines traditional concatenation with channel and spatial attention, significantly improving the quality of multi-scale feature fusion. The experimental results demonstrate that the proposed method achieves notable performance improvements across multiple public benchmark data sets. 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