Aiming at the problem of target misdetection and missing detection caused by large size changes and mutual occlusion in UAV aerial images, a lightweight target detection algorithm based on YOLOv8s is proposed by integrating multi-scale features. In the backbone network, lightweight multi-scale convolutional EMSC is used to replace Bottleneck in C2f module, which enhances the expression ability of multi-scale features. The lightweight upsampling operator Dysample is introduced into the neck network to capture the fine features of the image. Task Aligned Assigner hyperparameters are optimized to solve the problem of sample imbalance during training. Finally, the system of visual interface is designed, and the object of aerial photography is detected by visual interface. The simulation on the data set VisDrone2019 shows that the accuracy and recall rate of the algorithm are improved by 2.4% and 3.3% respectively compared with the benchmark algorithm, and mAP0.5 is improved by 3.5%, effectively improving the effect of aerial photography target detection. The model generalization experiment is carried out on UAVDT data set, and the effect is better than other classical algorithms.