Aiming at the UAV aerial image detection task, there are problems of tiny target size and complex background environment, which often lead to leakage and misdetection, this paper proposes a small target detection algorithm WT-YOLO based on YOLOv11 for aerial images. First of all, taking into account the problem that UAV aerial images are generally small targets, the structure of the YOLOv11 necking network is adjusted, and the output feature map is changed size, which improves the algorithm's ability to detect small targets. Secondly, the structure of Bottleneck and C3k2 module, named C3k2-WT, is redesigned in combination with WTConv to realize the efficient extraction of features. Again, Focal-Modulation is introduced to replace SPPF, which makes the model more robust in dealing with complex scenes by focusing and modulating the features at different spatial scales; finally, the shared convolution detection head is designed to reduce the number of parameters of the model through the convolution sharing mechanism, while enhancing the global information fusion capability between feature maps. The experiments of the improved algorithm on the VisDrone2019 dataset show that compared with the base YOLOv11s model, the accuracy (P), recall (R), and detection precision (mAP50) are improved by 5.6%, 5.9%, and 7.5%, respectively, and the number of params decreases by about one-fourth, which shows a good performance compared with other algorithms.