A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99%@en