Aiming at the problems of small size,large number of targets and complex background in UAV aerial images,a UAV aerial target detection algorithm based on improved YOLOv4-tiny is proposed.On the basis of the original network,the algorithm expands the scope of detection scale,improves the matching degree for targets of different sizes,and fuses deep semantic information with shallow semantic information from bottom to top to enrich the feature information of small targets.Meanwhile,the attention mechanism module is introduced to conduct secondary screening of the feature information of the region of interest at each scale behind the backbone network.So as to filter the redundant feature information,and retain the key feature information.Compared with that of the original network,the average accuracy of the proposed algorithm is improved by 5.09% on the basis of real-time performance,and the experimental results show that the proposed algorithm has good comprehensive performance.