Advertisement

Real-Time Drone Detection Using Deep Learning Approach

  • Manjia Wu
  • Weige Xie
  • Xiufang Shi
  • Panyu Shao
  • Zhiguo Shi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

The arbitrary use of drones poses great threat to public safety and personal privacy. It is necessary to detect the intruding drones in sensitive areas in real time. In this paper, we design a real-time drone detector using deep learning approach. Specifically, we improve a well-performed deep learning model, i.e., You Only Look Once, by modifying its structure and tuning its parameters to better accommodate drone detection. Considering that a robust detector needs to be trained using a large amount of training images, we also propose a semi-automatically dataset labelling method based on Kernelized Correlation Filters tracker to speed up the pre-processing of the training images. At last, the performance of our detector is verified via extensive experiments.

Keywords

Drone detection Deep learning Visual detection 

Notes

Acknowledgments

This work was supported by NSFC under Grant 61772467, Zhejiang Provincial Natural Science Foundation of China under Grant LR16F010002, 973 Project under Grant 2015CB352503, the Fundamental Research Funds for the Central Universities (2017XZZX009-01), and China Postdoctoral Science Foundation funded project.

References

  1. 1.
    Wargo, C., Snipes, C., Roy, A., Kerczewski, R.: UAS industry growth: forecasting impact on regional infrastructure, environment, and economy. In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1–5. IEEE (2016)Google Scholar
  2. 2.
    Chang, X., Yang, C., Wu, J., Shi, X., Shi, Z.: A surveillance system for drone localization and tracking using acoustic arrays. In: 2018 IEEE 87th Vehicular Technology Conference (2018)Google Scholar
  3. 3.
    Chang, X., Yang, C., Shi, X., Li, P., Shi, Z., Chen, J.: Feature extracted DOA estimation algorithm using acoustic array for drone surveillance. In: 2018 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (2018)Google Scholar
  4. 4.
    Yang, C., Wu, Z., Chang, X., Shi, X., Wo, J., Shi, Z.: DOA estimation using amateur drones harmonic acoustic signals. In: 2018 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (2018)Google Scholar
  5. 5.
    Shi, X., Yang, C., Xie, W., Liang, C., Shi, Z., Chen, J.: Anti-drone system with multiple surveillance technologies: architecture, implementation, and challenges. IEEE Commun. Mag. 56(4), 68–74 (2017)CrossRefGoogle Scholar
  6. 6.
    Chen, J., Kang, H., Wang, Q., Sun, Y., Shi, Z., He, S.: Narrowband internet of things: implementations and applications. IEEE Internet Things J. 4(6), 2309–2314 (2017)CrossRefGoogle Scholar
  7. 7.
    Sevil, H.E., Dogan, A., Subbarao, K., Huff, B.: Evaluation of extant computer vision techniques for detecting intruder sUAS. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 929–938. IEEE (2017)Google Scholar
  8. 8.
    Hwang, S., Lee, J., Shin, H., Cho, S., Shim, D.H.: Aircraft detection using deep convolutional neural network in small unmanned aircraft systems. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, p. 2137 (2018)Google Scholar
  9. 9.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing, vol. 1, pp. 582–585. IEEE (1994)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893. IEEE Computer Society (2005)Google Scholar
  12. 12.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.J.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)Google Scholar
  14. 14.
    Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587. IEEE Computer Society (2014)Google Scholar
  15. 15.
    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).  https://doi.org/10.1007/978-3-319-10578-9_23CrossRefGoogle Scholar
  16. 16.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  17. 17.
    Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788. IEEE Computer Society (2016)Google Scholar
  18. 18.
    Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR, pp. 6517–6525. IEEE Computer Society (2017)Google Scholar
  21. 21.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA, pp. 1027–1035. SIAM (2007)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Manjia Wu
    • 1
  • Weige Xie
    • 1
    • 2
  • Xiufang Shi
    • 1
    • 2
  • Panyu Shao
    • 1
    • 2
  • Zhiguo Shi
    • 2
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina
  2. 2.College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina

Personalised recommendations