With the continuous expansion of drone application scenarios, small object detection in aerial images has become a research hotspot in the field of computer vision. In view of the problems that small object features are not obvious, complex backgrounds lead to false detection and missed detection, and the existing algorithms are difficult to balance detection accuracy and real-time performance, this paper proposes an aerial image small object detection algorithm FST-RTDETR based on RT-DETR to solve these problems. First, FasterNet is combined with the EMA attention mechanism, and the structure of the Basic Block module of the original module is redesigned to improve the network operation speed and the accuracy of visual tasks. Secondly, in order to solve the problems of excessive calculation and more time-consuming post-processing after adding the traditional P2 detection layer, this study propose to use the P2 feature layer based on the original CCFM architecture to obtain features rich in small object information through SPDConv and give them to P3 for fusion, and then use the CSP idea and Omni-Kernel to improve CSP-OmniKernel for feature integration, effectively learn the feature performance from global to local, and finally reduce the missed detection rate, false detection rate and improve the detection performance of small objects. Finally, in order to simplify the loss function calculation process, improve regression efficiency and accuracy, and have a more comprehensive loss consideration, this study use inner-MPDIoU to replace the original GIoU. Experiments on the improved algorithm on the VisDrone2019 dataset show that the FST-RTDETR model achieves a detection accuracy of 49.6%, which is 2.1% higher than the original RT-DETR model. The FST-RTDETR model significantly improves the object detection performance of drone images, improves model efficiency, and shows good performance compared to other algorithms.