Performance evaluation of objects detection and identification on an image from special optoelectronic systems for airfield observations

System analysis, control and data processing


Аuthors

Selvesyuk N. I.1*, Veselov Y. G.2**, Gaydenkov A. V.3***, Ostrovsky A. S.2****

1. State Institute of Aviation Systems, 7, Victorenko str., Moscow, 125319, Russia
2. Bauman Moscow State Technical University, MSTU, 5, 2-nd Baumanskaya, Moscow, 105005, Russia
3. OJSC «Onboard Air Navigation Systems», 12-15, Bolshaya Novodmitrovskaya str., Moscow, 127015, Russia

*e-mail: nis@gosniias.ru
**e-mail: fukunaga@inbox.ru
***e-mail: gaidenkov@mail.ru
****e-mail: aleksandr_ostrovsky@mail.ru

Abstract

The article considers the problem of determining the dependencies of detection and recognition characteristics of various objects in the airfield sector of observation from the equipment parameters and observation conditions. It describes the methods for determining a priori and posteriori estimates of image quality indicators obtained by optical-electronic observation systems. The article gives scientifically based recommendations on the image spatial-frequency characteristics reduction to the optimum values according to the criteria set by the operator performing visual analysis.

A posteriori assessment is based on the tests including the object of interest of a given class against the background of the underlying surface. The subject of the assessment is the image of the scene (object and background). Observation characteristics of the scene are determined by the evaluation of the geometric and contrasting characteristics of the image of the object of interest, the frame size and the problem solving time. The proposed a posteriori estimation method of the characteristics of observation of objects of interest allows estimate the probability of object detecting on digital images when conducting field tests with a minimum of computational costs and special equipment.

The a priori estimate is based on calculations including parameters of the optical-electronic system, with those obtained using special test equipment among them, as well as the required observation conditions. The article presents and describes in detail the a priori method for estimating the characteristics of observing objects of interest, proposed by the authors. The a priori assessment of the observation characteristics is based on the modulation transfer functions apparatus, as well as the threshold modulation characteristics. It consists in automatically estimating the resolution in the laboratory using experimentally obtained transfer characteristics based on test objects of various configurations. It is shown, that the proposed method provides the possibility of a reliable analytical evaluation of the observation characteristics for arbitrary ranges, sizes and contrasts of the object of interest, fields of view of the optical-electronic system and meteorological conditions.

The prospects for the development of methodological apparatus for determining the characteristics of objects detection and recognition are considered. The main attention is paid to the implementation of algorithm for the search function of the eye transfer function, the corresponding camera transfer function, as well as methods for characteristics assessing of the camera ringing.

Application of the method of reliable a priori evaluation of the observation characteristics reduces the material and time costs of conducting tests of video surveillance systems of airfield objects and is of great practical interest. However, the a priori assessment (and method) requires calibration and verification based on the a posteriori direct field test method.

The presented methodological apparatus can be used for the tasks of remote airfield control services using non-radar technical means of monitoring the situation on the airfield.

Keywords:

optoelectronic systems, object detection, object recognition, ground-based observation systems, image quality indicators, modulation transfer function, visual analyzer

References

  1. Kovalenko V.P. Optiko-elektronnoe razvedyvatel’noe oborudovanie letatel’nykh apparatov (Aircraft optical-electronic reconnaissance equipment), Moscow, VVIA im. prof. N.E. Zhukovskogo, 1990, 182 p.

  2. Veselov Yu.G., Ostrovskii A.S., Sel’vesyuk N.I., Krasavin I.V. Izvestiya YuFU. Tekhnicheski nauki, 2013, no. 3 (140), pp. 84 – 89.

  3. Smit J.T. Manual of Color Aerial Photography, Virginia, American Society of Photogrammetri, 1968, 550 p.

  4. de Jong A.N., Winkel H., Ghauharali R.I. IR sensor performance testing with a double-slit method, Proc. SPIE 4372, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XII, 2001. Doi: 10.1117/12.439141.

  5. Soel M.A., Irwin A., Gaultney P., White S.G., McHugh S.W. High-end infrared imaging sensor evaluation system, Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, 2002. Doi: 10.1117/12.477456.

  6. Harvey J.E., Rockwell R.A. Performance characteristics of phased arrays and thinned aperture optical telescopes, Reflective Optics, Proc. SPIE, 1987, vol. 751, pp. 62 – 71.

  7. Gorbulin V.I., Khodor M.A. Trudy MAI, 2018, no. 100, available at: http://trudymai.ru/eng/published.php?ID=93426

  8. Travnikova N.P. Effektivnost’ vizual’nogo poiska (The effectiveness of visual search), Moscow, Mashinostroenie, 1985, 128 p.

  9. Mitra Sanjit K. Digital Signal Processing: A Computer-Based Approach, Mcgraw Hill, 1997, 864 p.

  10. Battiato S., Gallo G., and Stanco F. A locally-adaptive zooming algorithm for digital images, Image and Vision Computing, 2002, vol. 20, pp. 805 – 812.

  11. 11.Chen M., Huang C. and Lee W. A fast edge-oriented algorithm for image interpolation, Image and Vision Computing, 2005, vol. 23 (9), pp. 791 – 798.

  12. Zhivichin A.N., Sokolov V.S. Deshifrirovanie fotograficheskikh izobrazhenii (Decoding of photographic images), Moscow, Nedra, 1980, 253 p.

  13. Zhivichin A.N., Poddubnyi S.I. Izvestiya vuzov. Geodeziya i aerofotos«emka, 1978, no. 1, pp. 34 – 41.

  14. Veselov Yu.G., Danilin A.A., Karpikov I.V., Tikhonychev V.V. Problemy bezopasnosti poletov, 2009, no. 2, pp. 21 – 25.

  15. Li X., Orchard M. T. New edge-directed interpolation, IEEE Transactions on Image Processing, 2001, vol. 10 (10), pp. 1521 – 1527.

  16. Muresan D., Parks T. Adaptively quadratic (aqua) image interpolation, IEEE Transactions on Image Processing, 2004, vol. 13 (5), pp. 690 – 698.

  17. Hou H., Andrews H. Cubic splines for image interpolation and digital filtering, IEEE Transactions on Acoustics, Speech and Signal Processing, 1978, vol. 26 (6), pp. 508 – 517.

  18. Gaidenkov A.V., Veselov Yu.G., Ostrovskii A.S., Sel’vesyuk N.I. Yubileinaya vserossiiskaya nauchno-tekhnicheskaya konferentsiya “Aviatsionnye sistemy v XXI veke”. Sbornik tezisov dokladov. (Moscow, 26 – 27 May 2016), Moscow, GosNIIAS, 2016, pp. 197.

  19. Gaidenkov A.V., Veselov Yu.G., Ostrovskii A.S. XIII Vserossiiskaya nauchno-tekhnicheskaya konferentsiya “Nauchnye chteniya po aviatsii, posvyashchennye pamyati N.E. Zhukovskogo”. Sbornik dokladov. Moscow, Izdatel’skii dom Akademii N.E. Zhukovskogo, 2016, pp. 371 – 375.

  20. Zinov’ev Yu.S., Mishina O.A., Glushchenko A.A. Trudy MAI, 2018, no. 101, available at: http://trudymai.ru/eng/published.php?ID=96976


Download

mai.ru — informational site MAI

Copyright © 2000-2019 by MAI

Вход