In the view of UAVs,target scales have large differences,the detection scenarios are complex, and the targets are small and dense,which will lead to low detection accuracy.To solve the problems,a real-time target detection algorithm based on the improved YOLOv5n is proposed.Firstly,the capability of Convolutional Neural Network (CNN) for extracting effective information from the feature map is improved by introducing the Efficient Channel Attention (ECA) module.Secondly,the Adaptively Spatial Feature Fusion (ASFF) module is added after the output of the Feature Pyramid Network (FPN) to improve recognition accuracy of feature maps at different scales.Then,EIoU loss function is used to calculate the difference value between the prediction frame and the target frame to speed up convergence and improve detection accuracy.Finally,improvements are made to the detection head of YOLOv5n to optimize the models detection performance on small targets.Training and testing are carried out on VisDrone dataset.In comparison with the basic YOLOv5n model,the enhanced model improves mAP50 by 6.1 percentage points at 640×640 resolution,and improves mAP50 by 7.1 percentage points at 1504×1504 resolution.Meanwhile,the detection speed of the improved model is higher than 22 frames per second on hardware.The proposed model ensures a high enough detection speed while improving the accuracy,which is more suitable for real-time detection of small targets by UAVs.