Skip to main content
Log in

Technology for the Visual Inspection of Aircraft Surfaces Using Programmable Unmanned Aerial Vehicles

  • PATTERN RECOGNITION AND IMAGE PROCESSING
  • Published:
Journal of Computer and Systems Sciences International Aims and scope

Abstract

The technology for the visual inspection of aircraft surfaces using programmable unmanned aerial vehicles (drones) is presented. When developing the technology, special attention is paid to the problem of the drone’s indoor navigation where the signal from satellite positioning systems is weak or there is no signal, as well as to the development of algorithmic programs and software for detecting both the drone and damage to the aircraft surface based on video analysis. The results of testing the technology in enclosed spaces under conditions close to the expected operating conditions are given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. A. Sinitskii, “Drones inspect commercial aircraft,” Aviatransp. Obozr., Prilozh. Bespilot. Aviats., No. 189, 63–64 (2018).

  2. V. N. Vapnik, Statistical Learning Theory (Wiley, New York, 1998).

    MATH  Google Scholar 

  3. Y. LeCun, B. Boser, and J. S. Denker, “Backpropagation applied to handwritten zip code recognition,” Neural Comput. 1, 541–551 (1989).

    Article  Google Scholar 

  4. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proceedings of the International Conference on Learning Representations, San Diego, CA,2015.

  5. L. Songtao, D. Huang, and Y. Wang, “Receptive field block net for accurate and fast object detection,” in Proceedings of the European Conference on Computer Vision, Munich, Germany,2018.

  6. E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017).

    Article  Google Scholar 

  7. V. Jain and S. H. Seung, “Natural image denoising with convolutional networks,” in Proceedings of the International Conference on Neural Information Processing Systems, Vancouver, Canada,2008.

  8. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016).

    Article  Google Scholar 

  9. O. Russakovsky, J. Deng, and H. Su, “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vision 115, 211–252 (2015).

    Article  MathSciNet  Google Scholar 

  10. D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, “Microsoft COCO: common objects in context,” in Proceedings of the European Conference on Computer Vision, Zurich, Switzerland,2014.

  11. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: surpassing human-level performance on ImageNet classification,” in Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile,2015.

  12. Yu. B. Blokhinov, “A method for automatically determining orientation elements of an orbital station from reference model images of its nodes,” Izv. Vyssh. Uchebn. Zaved., Geodez. Aerofotos’emka, No. 2, 13–19 (2011).

    Google Scholar 

  13. J. Redmon and A. Farhadi, “Yolo V3: an incremental improvement,” arXiv: 1804.02767 (2018).

  14. A. P. Mikhailov and A. G. Chibunichev, Photogrammetry (MIIGAiK, Moscow, 2016) [in Russian].

  15. D. C. Brown, “Close-range camera calibration,” Photogrammetr. Eng. 37, 855–866 (1971).

    Google Scholar 

  16. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge Univ. Press, Canberra, Australia, 2004).

    Book  Google Scholar 

Download references

Funding

This work was supported by the Russian Foundation for Basic Research, project no. 17-08-00191 a.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yu. B. Blokhinov or V. A. Gorbachev.

Additional information

Translated by O. Pismenov

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blokhinov, Y.B., Gorbachev, V.A., Nikitin, A.D. et al. Technology for the Visual Inspection of Aircraft Surfaces Using Programmable Unmanned Aerial Vehicles. J. Comput. Syst. Sci. Int. 58, 960–968 (2019). https://doi.org/10.1134/S1064230719060042

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064230719060042

Navigation