%0 Journal Article
%T Research on UAV Target Detection Based on Improved YOLOv11
%A Guangdong Ren
%A Junyi Wu
%A Weizheng Wang
%J Journal of Computer and Communications
%P 74-85
%@ 2327-5227
%D 2025
%I Scientific Research Publishing
%R 10.4236/jcc.2025.133006
%X 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.
%K YOLOv11
%K Aerial Images
%K Small Target Detection
%K Single-Object Detection
%K Dynamic Detection Head
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=141510