An improved YOLOv7 target detection algorithm is proposed to solve such problems as sharp changing of target scales,low detection accuracy and high missing rate of small targets in UAV acquisition scenarios.Firstly,a minimal target detection layer is added on the basis of the original YOLOv7 to adapt to targets at different scales and reduce the missed detection rate of small targets.Secondly,the non-parametric attention mechanism is introduced into the feature fusion network,and an MP-SimAM module is constructed based on the attention mechanism to fuse more important feature information.Finally,a new box regression loss function,named SCIoU Loss,is proposedto further improve the model's convergence speed and detection accuracy.The experimental results show that the model performs well on VisDrone 2019 dataset.The mAP50 of the proposed algorithm model reaches 44.0% on the test set,which is 2.6 percentage points higher than that of the benchmark model YOLOv7.The detection effect of small targets is significantly improved.