Consumer-level UAVs have small scale, low fly speed and height, existing deep learning methods hardly achieve high detection accuracy and good robustness on detecting UAVs. In order to address this problem, this paper develops an improved YOLOv4 algorithm with feature enhanced module named as FEM-YOLOv4 for UAVs detection. Firstly, according to the characteristics of UAVs, this paper reduces the subsampling multiple of CSPDarkNet to improve the backbone network and make full use of shallow features containing detailed information. Secondly, this paper introduces the feature enhancement module to replace the SPP module. The feature enhancement module includes multiple branches and dilated convolution, and it obtains different levels of semantic information, which is beneficial to enhance the detailed semantic features and the detection capabilities of the network. Thirdly, delete the PAN module to improve the feature pyramid, and compress the depth of each detection layer to highlight the detailed and semantic information of the feature maps. Finally, the anchor box is initialized by the K-means++ algorithm to make the model more suitable for predicting the UAV targets. Compared with the six target detection algorithms, the experimental results show that the mAP and Recall of FEM-YOLOv4 algorithm reach 89. 48% and 97. 4% respectively, which are superior to other algorithms, and the average detection speed is 0. 042 s.