Aiming at the problem that the target size of the UAV aerial image is too small and contains less feature information, which leads to the unsatisfactory detection effect of the existing detection algorithm on small objects, a UAV aerial photography based on feature aggregation and multi-collaborative feature interaction is proposed. First of all, in view of the insufficient feature extraction of the backbone network, Swin Transformer is selected as the RetinaNet backbone network to enhance the global information extraction ability of the algorithm. Secondly, in order to improve the detection ability of remote targets, a small target feature aggregation network is proposed, which can fully integrate the details of small targets in shallow feature maps. Finally, in order to further improve the detection performance of multi-scale targets, a new multiple collaborative feature interaction module is proposed to make the low-level feature information flow to the high-level. Experimental results on VisDrone2019-DET, a public UAV aerial photo data set, show that compared with the original RetinaNet baseline network detection precision increased by 7. 6%, the proposed algorithm has better detection effect for small targets.