Existing models achieve high accuracy in small object detection but often overlook the rotational and dense characteristics of targets. 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–ARC module that enhances the model’s 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. Compared with the advanced YOLO11n model, our approach achieves gains of 1.6% on VISDRONE, 1.4% on UAVDT, 0.2% on CARPK, and 0.1% on UAVROD, further validating its effectiveness across diverse UAV detection scenarios.

This paper presents a redesigned YOLO-based model tailored for small-object detection in drone applications. To enhance its performance in detecting small and blurry targets, this study introduces the C3_CAA module to refine feature maps, integrates the CPA module and SI-IoU to improve detection accuracy, and incorporates channel and spatial attention mechanisms to further enhance target localization and identification performance.The experimental results indicated that the proposed method performs well on multiple datasets. The mAP value increases by 2% on the VISDRONE dataset, 1.6% on the UAVDT dataset, 0.9% on the CARPK dataset, and 1% on the UAVROD data set.

This paper proposes a redesigned YOLO-based model tailored for small object detection in UAV applications. To address the rotational characteristics of multi-scale objects in aerial imagery, this paper introduces a novel C3k2-ARC module designed to enhance the model’s capability in extracting features from rotated targets, thereby improving detection accuracy. Furthermore, we propose a novel feature fusion module named CL-Concat, which combines the traditional concatenation operation with channel attention and spatial attention mechanisms. This design significantly enhances the quality of multi-scale feature fusion. Experimental results demonstrate that the proposed approach achieves superior performance across multiple public benchmarks, with mAP improvements of 1.6% on the VISDRONE dataset, 1.4% on UAVDT, 0.2% on CARPK, and 0.1% on UAVROD.