In order to solve the security risks caused by UAV "black flight", aiming at the problems of large model parameters and long training time of the existing UAV target detection algorithm based on deep learning, a lightweight real-time object detection algorithm for UAV based on YOLOv5 (ED-YOLOv5s) was proposed. Firstly, the feature extraction part of YOLOv5 backbone network was reconstructed by combining the lightweight model efficient model (EMO). Secondly, normalized gaussian wasserstein distance (NWD) and scale factor were introduced to calculate the similarity between candidate boxes to partially replace IoU. Then, SimAM was introduced to optimize the weight distribution and improved the detection accuracy, so as to achieved the effect of optimizing the Backbone and Head of YOLOv5 network. Finally, the model size is 4.57 M. ED-YOLOv5s lightweight UAV detection algorithm with floating point computation of 26.1 (GFLOPs). The experimental data shows that the improved algorithm improves the detection accuracy and realizes the model lightweight. The proposed algorithm achieves 96.7% AP@50 value on the DUT Anti-UAV dataset, and the detection speed reaches 37 frame/s on the RTX2060 GPU.