To address the challenges of small object detection that information loss during the down-sampling and neglect of target details by deep features, this research proposes a small object detection algorithm based on the spatial-frequency separated cross-attention Swin Transformer (SF-SCST). The SF-SCST algorithm distinguishes objects from the background in the frequency domain through the wavelet decomposition and feature concatenation (WDFC) module and a feature channel spatial-frequency decomposition and fusion (SFDF) module. Then, these features are fused with spatial domain information to enhance the target contour, effectively preserving small object features during down-sampling. Additionally, the cross-self attention Swin Transformer (CS-swin) module performs dual-attention calculation on deep and shallow features to supplement the small object information lost in the deep features and to capture the contextual information of targets. Experimental results show that the SF-SCST algorithm achieves the mean average precision with intersection over union of 0.5 (mAP₅₀) of 69.3% and 46.0% on the UAV-DA and VisDrone datasets, respectively. The performance of proposed algorithm is superior compared with the other six algorithms, significantly improving the detection accuracy of small objects.