Du, D., Zhu, P., Wen, L., et al. (2019) VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops.

has been cited by the following article:

• TITLE: Research on UAV Target Detection Based on Improved YOLOv11

  AUTHORS: Guangdong Ren, Junyi Wu, Weizheng Wang

  KEYWORDS: YOLOv11, Aerial Images, Small Target Detection, Single-Object Detection, Dynamic Detection Head

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.3, March 25, 2025

  ABSTRACT: In response to the challenges of small object detection in UAV aerial photography, such as complex backgrounds, tiny targets, dense targets, and edge deployment, the YOLOv11n model was improved. Specifically, an EfficiBackbone module was designed for the backbone part, the C3K2 was improved using the RipViT block in the Neck part, and the original detection head was replaced with a dynamic detection head. The improved YOLOv11 network was thus completed. Experimental results show that the model has significantly improved mAP@0.5 and mAP@0.5:0.95 on the VisDrone2019 dataset, proving the effectiveness of the model.