Critical Infrastructure Security Against Drone Attacks Using Visual Analytics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


The recent developments in the field of unmanned aerial vehicles (UAV or drones) technology has generated a lot of interdisciplinary applications, ranging from remote surveillance of energy infrastructure, to agriculture. However, in the context of national security, low-cost drone equipment has also been viewed as an easy means to cause destructive effects against national critical infrastructures and civilian population. Addressing the challenge of real-time detection and continuous tracking, this paper proposed presents a holistic architecture consisting of both software and hardware design. The software-based video analytics component leverages upon the advancement of Region based Fully Convolutional Network model for drone detection. The hardware component includes a low-cost sensing equipment powered by Raspberry Pi for controlling the camera platform for continuously tracking the orientation of the drone by streaming the video footage captured from the long-range surveillance camera. The novelty of the proposed framework is twofold namely the detection of the drone in real-time and continuous tracking of the detected drone through controlling the camera platform. The framework relies on the capability of the long-range camera to lock into the drone and subsequently track the drone through space. The analytics processing component utilises the NVIDIA\(\circledR \) GeForce\(\circledR \) GTX 1080 with 8 GB GDDR5X GPU. The experimental results of the proposed framework have been validated against real-world threat scenarios simulated for the protection of the national critical infrastructure.


Intruder drones Surveillance camera Critical infrastructure Deep-learning Drone detection and tracking Raspberry Pi 



This work is partially funded by the European Union Horizon 2020 research and innovation program under grant agreement No. 740898 (DEFENDER IP project) and grant agreement No. 787123 (PERSONA RIA project).


  1. 1.
    Cehovin, L., Leonardis, A., Kristan, M.: Visual object tracking performance measures revisited. CoRR abs/1502.05803 (2015).
  2. 2.
    Coluccia, A., et al.: Drone-vs-bird detection challenge at IEEE AVSS2017. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, August 2017.
  3. 3.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. CoRR abs/1605.06409 (2016).
  4. 4.
    Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 11–18 December 2015, pp. 1440–1448 (2015). IEEE International Conference on Computer Vision, Amazon; Microsoft; Sansatime; Baidu; Intel; Facebook; Adobe; Panasonic; 360; Google; Omron; Blippar; iRobot; Hiscene; nVidia; Mvrec; Viscovery; AiCure
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015).
  7. 7.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. CoRR abs/1404.7584 (2014).
  8. 8.
    Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861 (2017).
  9. 9.
    Liu, W., et al.: SSD: single shot multibox detector. CoRR abs/1512.02325 (2015).
  10. 10.
    Mejias, L., McNamara, S., Lai, J., Ford, J.: Vision-based detection and tracking of aerial targets for UAV collision avoidance. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, 18–22 October 2010, pp. 87–92 (2010).
  11. 11.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, 27–30 June 2016, pp. 779–788. IEEE Computer Society; Computer Vision Foundation (2016).
  12. 12.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). Scholar
  13. 13.
    Rozantsev, A., Lepetit, V., Fua, P.: Detecting flying objects using a single moving camera. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 879–892 (2017). Scholar
  14. 14.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015).
  15. 15.
    Wu, Y., Lim, J., Yang, M.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Venaka Media LimitedLondonUK

Personalised recommendations