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<p>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.</p>

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TOE-YOLO: accurate and efficient detection of tiny objects in UAV imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月27日

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  • Haimin Yan,
  • Xiangbo Kong,
  • Tomoyasu Shimada,
  • Hiroyuki Tomiyama

摘要

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.