Aerial Image Object Detection Model Based on CFE-YOLOv11

GU Chengjie; PENG Junming; ZHU Dongjun; ZHANG Junjun; ZHENG Yabing

doi:10.13501/j.cnki.42-1908/n.2025.12.001

Abstract：To address issues such as large target scale variations,target occlusion,and large model parameters in unmanned aerial vehicle(UAV)aerial images,the context feature enhancement-you only look once version 11(CFE-YOLOv11)model was proposed for aerial image target detection.Firstly,a lightweight downsampling convolution(LDC)module was designed,which enhanced feature information through dual-path downsampling and improved cross-channel information interaction using channel shuffling,thereby reducing the number of model parameters.Secondly,a convolutional three-scale kernel-adaptive dual-path separated and enhancement attention(C3k2-SEA)module based on separated and enhanced attention was designed to improve the feature extraction capability of the model.Meanwhile,multi-branch spatial and channel attention(MSCA)module was proposed to enhance the model′s ability to extract features of targets at different scales.Finally,dual loss optimization(DLO)module was used to optimize the detection effect of the model through a differential gradient gain allocation mechanism.The results showed that,compared with the YOLOv11 model,the CFE-YOLOv11 model achieved 2.1 and 2.0 percentage points,respectively,in mean average precision(mAP)at an intersection over union(IoU)threshold of 0.50 were achieved by the CFE-YOLOv11 model on the visual drones(VisDrone)and remote sensing object detection(RSOD)datasets,respectively,while the number of parameters was reduced by 15.4%.The CFE-YOLOv11 model not only improved the detection accuracy of aerial images but also alleviated the problems of model missed detection and false detection,providing an efficient solution for accurate detection of aerial image targets.