Aiming at the problems of low accuracy and poor real-time performance of drogue target recognition in aerial refueling caused by scene illumination changes and environmental occlusion, a drogue target detection algorithm based on cascaded Snappy-CenterNet deep network is proposed. On the basis of CenterNet network, HourglassNet is used as the backbone network, the bottleneck structure is improved and the central pooling method is introduced to optimize the overall network structure, and the overall detection accuracy is improved through the cascaded network. The experimental results show that the proposed algorithm can reliably detect drogue targets in a variety of complex scenes, and the precision and recall of the detection results can reach 99%, the position accuracy and region accuracy can reach 99% and 96%, respectively, and the update rate can reach 33.68 Hz, which meets the requirements of aerial refueling near vision navigation for drogue recognition.