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<p>In UAV aerial image target detection, the presence of small-scale objects, complex backgrounds, and weak illumination leads to difficulties in feature extraction and low detection accuracy. To address these issues, this paper proposes an aerial image target detection algorithm named Dual-YOLO. First, a parallel dual-path backbone network is designed to achieve complementary feature extraction, thereby enhancing the feature extraction capability for targets. Second, a bidirectional feature pyramid network (BiFPN) structure is implemented in the neck to optimize multi-scale feature fusion, which enhances feature representation capabilities through its bidirectional cross-scale connectivity. Finally, a dynamic head framework is employed to unify the object detection head and the attention mechanism, thereby enhancing overall detection performance. Experimental results show that the Dual-YOLO algorithm achieves mean Average Precision at 50% IoU (mAP50) scores of 43.1% and 76.3% on the VisDrone2019 and HazyDet datasets, respectively, outperforming the baseline model by 9.3% and 6.4% and significantly enhancing detection accuracy for aerial targets.</p>

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Dual-YOLO: dual-path UAV aerial image target detection algorithm

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Houhong Liu,
  • Fei Tan,
  • Yuxin Jin

摘要

In UAV aerial image target detection, the presence of small-scale objects, complex backgrounds, and weak illumination leads to difficulties in feature extraction and low detection accuracy. To address these issues, this paper proposes an aerial image target detection algorithm named Dual-YOLO. First, a parallel dual-path backbone network is designed to achieve complementary feature extraction, thereby enhancing the feature extraction capability for targets. Second, a bidirectional feature pyramid network (BiFPN) structure is implemented in the neck to optimize multi-scale feature fusion, which enhances feature representation capabilities through its bidirectional cross-scale connectivity. Finally, a dynamic head framework is employed to unify the object detection head and the attention mechanism, thereby enhancing overall detection performance. Experimental results show that the Dual-YOLO algorithm achieves mean Average Precision at 50% IoU (mAP50) scores of 43.1% and 76.3% on the VisDrone2019 and HazyDet datasets, respectively, outperforming the baseline model by 9.3% and 6.4% and significantly enhancing detection accuracy for aerial targets.