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Embedded Vision System for Automated Drone Landing Site Detection

  • Patryk Fraczek
  • Andre Mora
  • Tomasz Kryjak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11114)

Abstract

This paper presents an embedded video subsystem used to classify the terrain, based on an image from a camera located under the drone, for the purpose of an automatic landing system. Colour and texture features, as well as decision trees and support vector machine classifiers were analysed and evaluated. The algorithm was supported with a shadow detection module. It was evaluated on 100 test cases and achieved over 80% performance. The designed video system was implemented on two embedded platforms – a Zynq SoC (System on Chip – Field Programmable Gate Array + ARM processor system) and a Jetson GPU (Graphic Processing Unit + ARM processor system). The performance achieved on both architectures is compared and discussed.

Keywords

Unmanned Aerial Vehicle (UAV) Safe landing site detection Decision Trees (DT) Support Vector Machine (SVM) Machine learning Digital image processing FPGA Zynq GPU 

Notes

Acknowledgements

The work presented in this paper was partially supported by the National Science Centre project no. 2016/23/D/ST6/01389 and Fundação para a Ciencia e a Tecnologia under the grant SFRH/BSAB/135037/2017.

References

  1. 1.
    Breiman, L., et al.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)zbMATHGoogle Scholar
  2. 2.
    Din, A., et al.: Embedded low power controller for autonomous landing of UAV using artificial neural network. In: 10th International Conference on Frontiers of Information Technology, Islamabad, pp. 196–203 (2012)Google Scholar
  3. 3.
    Fitzgerald, D., Walker, R., Campbell, D.: A vision based emergency forced landing system for an autonomous UAV. In: Proceedings Australian International Aerospace Congress Conference, Melbourne, Australia (2005)Google Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3(6), 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    Li, X.: A software scheme for UAV’s safe landing area discovery. AASRI Procedia 4, 230–235 (2013)CrossRefGoogle Scholar
  6. 6.
    Mejias, L., Fitzgerald, D.: A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 366–372. IEEE (2013)Google Scholar
  7. 7.
    Mestre, D., Fonseca, J., Mora A.: Monitoring of in-vitro plant cultures using digital image processing and random forests. In: ICPRS - IET 8th International Conference on Pattern Recognition Systems (2017)Google Scholar
  8. 8.
    Mora, A.D., et al.: A fuzzy multicriteria approach for data fusion. In: Fourati, H. (ed.) Multisensor Data Fusion From Algorithms and Architectural Design to Applications, pp. 109–126. CRC Press, Boca Raton (2015)Google Scholar
  9. 9.
    Mora, A., et al.: Land cover classification from multispectral data using computational intelligence tools: a comparative study. Information 8(4), 147 (2017)CrossRefGoogle Scholar
  10. 10.
    Mukadam, K., Sinh, A., Karani, R.: Detection of landing areas for unmanned aerial vehicles. In: 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1–5. IEEE (2016)Google Scholar
  11. 11.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  12. 12.
    Paglieroni, D.: Distance transforms: properties and machine vision applications. Comput. Vis. Graph. Image Process. Graph. Model. Image Process. 54(1), 57–58 (1992)Google Scholar
  13. 13.
    Ribeiro, R.A., et al.: FIF: a fuzzy information fusion algorithm based on multi-criteria decision making. Knowl.-Based Syst. 58, 23–32 (2014)CrossRefGoogle Scholar
  14. 14.
    Saqib, F., Dutta, A., Plusquellic, J., Ortiz, P., Pattichis, M.S.: Pipelined decision tree classification accelerator implementation in FPGA (DT-CAIF). IEEE Trans. Comput. 64(1), 280–285 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Simoes, L., Bourdarias, C., Ribeiro, R.: Real-time planetary landing site selection-a non-exhaustive approach. Acta Futur. 5, 39–52 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Automatics Computer Science and Biomedical EngineeringAGH University of Science and TechnologyKrakowPoland
  2. 2.Computational Intelligence Group of CTS/UNINOVAFCT, University NOVA of LisbonCaparicaPortugal

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