The issues of small UAV (unmanned aerial vehicle) target size, limited pixel coverage in images, weak texture detail information, and the difficulty in effectively extracting infrared UAV target features, which lead to low detection accuracy, were addressed by proposing a multi-scale learning-based target detection algorithm. A multi-scale feature fusion structure was constructed in the neck network of the model, and a multi-scale feature learning module was introduced. Features from both deep and shallow networks were cascaded to capture target features at multiple scales, enriching the semantic and feature information of the feature map, which significantly improved the detection accuracy of small UAV targets. During training, SIoU was used in place of CIoU loss, minimizing the network models loss and enhancing the regression accuracy. Experimental results demonstrate that, compared to other infrared small target detection algorithms and mainstream methods, the proposed approach effectively improves the detection accuracy of UAV targets and meet the detection accuracy requirements for UAV target detection in practical applications.