A machine vision based drogue detection and location algorithm was proposed for getting exactly drogue detection and location results in docking process of probe and drogue autonomous aerial refueling (AAR). Firstly, reliable drogue feature and classifier models were generated through Adaboost training based on numerous positive and negative samples; then, using the drogue model, the drogue can be detected on the drogue image sequences according to machine learning based detection and tracking technology; finally, used the image domain position of drogue, the three-dimensional (3-D) position of drogue relative to refueling probe can be calculated through a mapping model from 2-D image to 3-D space. Simulation results showed that the drogue detection rate was over 95%, time elapse was less than 4ms/frame, and the position measurement deviation was less than 7%, which can meet the requirements of AAR.