Aiming at the problem of low detection accuracy caused by complex background and dense distribution of small size targets in unmanned aerial vehicle （UAV）， this paper proposes a small target detection algorithm based on high resolution feature enhancement. Firstly， a high-resolution feature enhancement network is proposed， which expands the scale of the output feature map by reducing the sub-sampling times of the backbone. At the same time， the bilinear interpolation is introduced to reduce the loss of feature information after up-sampling， thereby preserving more semantic and detailed features. Secondly， the spatial pyramid pooling-fast module combined with the cross stage partial structure is embedded in the backbone to enhance the information fusion of local and global features， so as to obtain a larger receptive field. Finally， the mosaic-mixup data enhancement method is used to enhance the complexity of image background and improve the generalization ability of the model. Experimental results on the public dataset VisDrone 2019 show that compared with other mainstream algorithms such as the “ you only look once ”（YOLO） series， the mean average precision of the proposed algorithm has significantly improved. The advantages of the proposed algorithm have been verified in different scenarios， indicating that the algorithm has strong practicality for dense small target detection tasks in UAV aerial images.