With the rapid development and application of UAVs,the popularity of UAVs has also caused certain security risks to public security,military security and personal privacy.UAVs has the characteristics of high flying speed and small volume,so how to accurately and quickly find and locate the position of UAVs is a challenge.A real-time detection algorithm for YOLOv3 UAVs based on SandGlass Bottleneck Block is proposed.Firstly,the original three feature scales are extended to five feature scales to make full use of multi-scale information to help improve the detection accuracy of small targets.Then,the SandGlass Bottleneck Block is stacked as the backbone network part of the method,and the SandGlass Bottleneck Block is taken as a lightweight network to accelerate the model,which uses the channel attention mechanism to focus on more important channels in the splicing part after upsampling unformation and suppresses unfavorable information.In order to verify the proposed algorithm effectiveness,a UAVs data set is generated based on complex urban background.Experimental results show that the proposed algorithm can achieve 98.92% accuracy and a recall rate of 98.76%,achieves a real-time detection speed of 62.37 FPS on 1080Ti graphics card,the model weight is only 5.38 MiB,which further provides the possibility for real-time target detection on embedded platforms and mobile devices.