Drogue Detection for Autonomous Aerial Refueling Based on Adaboost and Convolutional Neural Networks

  • Yanjie Guo
  • Yimin DengEmail author
  • Haibin Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)


Autonomous aerial refueling (AAR) is an important capability for the future development of unmanned aerial vehicles (UAVs). A robust and accurate algorithm of detecting the drogue is crucial to such a capability. In this paper, we present an innovative algorithm based on the adaptive boosting algorithm and convolutional neural networks (CNN) classifier with improved focal loss (IFL). The IFL function addresses the sample imbalance during the training stage of the CNN classifier. The pytorch deep learning framework with the graphics processing units (GPUs) is used to implement the system. Real scenario images that contain drogue carried by UAVs are for training and testing. The results show that the algorithm not only accelerates the speed but also improves the accuracy.


Autonomous aerial refueling Adaboost CNN Sample imbalance 


  1. 1.
    Chen, C.I., Stettner, R.: Drogue tracking using 3D flash lidar for autonomous aerial refueling. In: Laser Radar Technology and Applications XVI, pp. 1–11. SPIE Press, Orlando (2011)Google Scholar
  2. 2.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Girshick, R.: Fast r-cnn. In: 17th Proceedings of IEEE International Conference on Computer Vision, pp. 1–9. IEEE Press, Santiago (2015)Google Scholar
  4. 4.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 29th IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE Computer Society, Las Vegas (2016)Google Scholar
  5. 5.
    Liu, W., et al.: Single shot multibox detector. In: The 14th European Conference on Computer Vision, pp. 21–37. ECCV Press, Amsterdam (2016)Google Scholar
  6. 6.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  7. 7.
    Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 (2017)
  8. 8.
    Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for adaboost. Mach. Learn. 42(3), 287–320 (2001)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: 14th Advances Neural Information Processing Systems, pp. 1311–1318. MIT Press, Cambridge (2001)Google Scholar
  10. 10.
    Ren, S.Q., He, K.M., Girshick, R., Sun, J.: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

Personalised recommendations