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<p>Drones are widely used in traffic management due to their wide inspection range and high flexibility. Among them, target detection based on drone images plays an important role. However, target detection using drones faces the problem of small targets and insufficient detailed information in the image, resulting in low accuracy of model detection and a high rate of false detection and missed detection. This paper proposes an improved small object detection algorithm, S-YOLOv11, to solve the above problems. First, an enhanced multi-branch auxiliary feature pyramid network (EMAFPN) is designed in the neck, inspired by the ideas of multi-branch auxiliary feature pyramid network (MAFPN) and bidirectional feature pyramid network (BiFPN) to strengthen the ability of effective feature information exchange and fusion between networks. Second, by introducing an efficient shared detail-enhanced convolutional detection head (ESDCDH), the model’s ability to locate, classify, and capture details is effectively improved. Finally, the learning ability of the model to small size and difficult-to-detect objects is enhanced by using the Intersection over Union with Normalized Wasserstein Distance and Minimum Point Distance (NWD-MPD-IoU) loss function. Experimental results demonstrate that compared with the original model, the S-YOLOv11 model improves mAP0.5, mAP0.5:0.95, P, and R on the public dataset VisDrone2019 by 11.8%, 8.1%, 9.7%, and 9.8%, respectively, and improves detection accuracy on the WiderPerson and CARPK datasets by 1.2% and 1.0%, respectively.</p>

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An improved YOLOv11 algorithm for small object detection in UAV images

  • 期刊论文

  • 发表日期：

    2025年5月2日

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  • Chishe Wang,
  • Xingqing Song,
  • Jie Wang,
  • Xinyun Yan

摘要

Drones are widely used in traffic management due to their wide inspection range and high flexibility. Among them, target detection based on drone images plays an important role. However, target detection using drones faces the problem of small targets and insufficient detailed information in the image, resulting in low accuracy of model detection and a high rate of false detection and missed detection. This paper proposes an improved small object detection algorithm, S-YOLOv11, to solve the above problems. First, an enhanced multi-branch auxiliary feature pyramid network (EMAFPN) is designed in the neck, inspired by the ideas of multi-branch auxiliary feature pyramid network (MAFPN) and bidirectional feature pyramid network (BiFPN) to strengthen the ability of effective feature information exchange and fusion between networks. Second, by introducing an efficient shared detail-enhanced convolutional detection head (ESDCDH), the model’s ability to locate, classify, and capture details is effectively improved. Finally, the learning ability of the model to small size and difficult-to-detect objects is enhanced by using the Intersection over Union with Normalized Wasserstein Distance and Minimum Point Distance (NWD-MPD-IoU) loss function. Experimental results demonstrate that compared with the original model, the S-YOLOv11 model improves mAP0.5, mAP0.5:0.95, P, and R on the public dataset VisDrone2019 by 11.8%, 8.1%, 9.7%, and 9.8%, respectively, and improves detection accuracy on the WiderPerson and CARPK datasets by 1.2% and 1.0%, respectively.