The real-time detection of aerial refueling drogue is an important prerequisite for the realization of autonomous aerial refueling. Since the existing object detection algorithms in aerial refueling drogue detection is susceptible to environmental interferences and may result in insufficient accuracy, a real-time detection algorithm for aerial refueling drogue based on the Transformer feature pyramid is proposed. Firstly, a new pooling attention-based Transformer feature pyramid structure TPN is proposed for backbone feature fusion to achieve more efficient feature map enhancement. Then, linear attention is used to reduce complexity of the attention mechanism in TPN, and the lightweight detection model DNet-LinTPN is proposed to reduce the memory consumption by 80%. The experimental results on the self-created air refueling drogue dataset show that the TPN-based model outperforms YOLOv7 in terms of accuracy, speed and model size under the same conditions. The lightweight detection model of DNet-LinTPN achieves an accuracy of 93.8%, which is a 9.4 percentage point improvement over YOLOv7-tiny, with a 67.2% reduction in the amount of parameters and a 45.2% reduction in the amount of operations, and the robustness is obviously improved.