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<p>Small object detection in unmanned aerial vehicle (UAV) aerial imagery is challenged by small object size, sparse feature information, and complex backgrounds. This paper presents AeroVision-DET, an aerial small object detection algorithm based on dynamic convolution and hierarchical attention fusion. We construct the Adaptive Receptive Field Network (ARFNet) based on the Multi-Scale Adaptive Feature Module (MSAFM) as the backbone, which achieves adaptive receptive field adjustment through dual-path feature enhancement blocks and multi-shape adaptive convolution. We design the Semantic-Spatial Fusion Module (SSFM), which adopts a multi-scale contextual attention mechanism to achieve semantically consistent and spatially sensitive feature fusion. We further propose the Efficient Feature Encoding Layer (EFEL), which integrates polarized linear attention and a frequency-modulated feed-forward network to model long-range dependencies under linear complexity. Experiments on VisDrone2019 show that AeroVision-DET achieves 24.2% AP, a 3.4% improvement over the RT-DETR-r18 baseline, with a 3.5% gain on small-object AP and a 27.0% parameter reduction while computational complexity remains essentially unchanged. With only 14.57&#xa0;M parameters and 56.9 GFLOPs, AeroVision-DET delivers 69.5 FPS real-time inference and is well-suited for deployment on resource-constrained edge devices such as embedded UAV platforms, while maintaining detection accuracy competitive with or superior to state-of-the-art (SOTA) models.</p>

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Aerial small object detection via dynamic convolution and hierarchical attention fusion for UAV imagery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Junxia Zhang,
  • Hao Zhong,
  • Gang Du,
  • Bing Zhang,
  • Hao Zhang

摘要

Small object detection in unmanned aerial vehicle (UAV) aerial imagery is challenged by small object size, sparse feature information, and complex backgrounds. This paper presents AeroVision-DET, an aerial small object detection algorithm based on dynamic convolution and hierarchical attention fusion. We construct the Adaptive Receptive Field Network (ARFNet) based on the Multi-Scale Adaptive Feature Module (MSAFM) as the backbone, which achieves adaptive receptive field adjustment through dual-path feature enhancement blocks and multi-shape adaptive convolution. We design the Semantic-Spatial Fusion Module (SSFM), which adopts a multi-scale contextual attention mechanism to achieve semantically consistent and spatially sensitive feature fusion. We further propose the Efficient Feature Encoding Layer (EFEL), which integrates polarized linear attention and a frequency-modulated feed-forward network to model long-range dependencies under linear complexity. Experiments on VisDrone2019 show that AeroVision-DET achieves 24.2% AP, a 3.4% improvement over the RT-DETR-r18 baseline, with a 3.5% gain on small-object AP and a 27.0% parameter reduction while computational complexity remains essentially unchanged. With only 14.57 M parameters and 56.9 GFLOPs, AeroVision-DET delivers 69.5 FPS real-time inference and is well-suited for deployment on resource-constrained edge devices such as embedded UAV platforms, while maintaining detection accuracy competitive with or superior to state-of-the-art (SOTA) models.