Method of assessing the influence of temperature contrast on the probability of object detection based on the results of laboratory tests of a thermal imaging electro-optical system


Аuthors

Krasnov A. M.1*, Shashkov S. N.2, Rumyantsev A. V.3

1. Closed Joint Stock Company «Technological Park of Cosmonautics «LINKOS»», Moscow, Shcherbinka, Russia
2. State Research Institute of aviation systems, Moscow, Russia
3. Сenter (control of integrated safety and security) the MoD RF , Moscow, Russia

*e-mail: a_krasnov@inbox.ru

Abstract

The main parameters characterizing the efficiency of thermal imaging electro-optical systems are the signal transfer function (SiTF), the frequency-contrast characteristic and noise. The article considers the definition of SiTF, and spatial and temporal components of 3-D noise from images obtained in laboratory conditions. Homogeneous images of the object and the background were synthesized from the obtained SiTF functions and 3-D noise components, then the temperature contrast and signal-to-noise ratio (SNR) were calculated from these images, and the dependence of the probability of detecting an object on SNR and temperature contrast was determined.
The article considers a method for assessing the influence of temperature contrast on the probability of object detection based on the results of laboratory tests of electro-optical systems, including: preparation of initial data and obtaining target images using electro-optical systems; image processing, calculation of SiTF functions, noise and its components; synthesis of background and target images; calculation of average values of object temperature, background, standard deviation of object temperature and background, standard deviation of noise from the object and background; calculation of temperature contrast and signal-to-noise ratio; calculation of object detection probability and plotting the dependence of detection probability on temperature contrast. 

Keywords:

equivalent temperature difference, temperature contrast, optical contrast, detection probability, signal transmission function, SiTF, 3-D noise and its components

References

  1. Ronald G. Driggers, Melvin H. Friedman, John W. Devitt, Orges Furxhi, Anjali Singh. Introduction to Infrared and Electro-Optical Systems - Third Edition. Artech House, 2022.
  2.  Gerald C. Holst. Electro-Optical Imaging System Performance - Fifth edition. JCD Publishing and SPIE Press, 2008.
  3. Bradley L. Preece, Eric A. Flug, "A wavelet contrast metric for the targeting task performance metric," Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 98200E (24 June 2016); doi: 10.1117/12.2223855
  4. Gerald C. Holst. Electro-Optical Imaging System Performance - Sixth edition. JCD Publishing and SPIE Press, 2017.
  5. W. Wan. “Passive IR Sensor Performance Analysis using Mathcad Modeling”. Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XX Proc. SPIE Vol. 7300 (2009); doi: 10.1117/12.815238.
  6. Charles C. Kim, "Improved formulas for CSNR with an example," Proc. SPIE 11406, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXI, 114060C (23 April 2020); doi: 10.1117/12.2551895.
  7. Krasnov A.M., Tregubenkov S.Yu., Rumyantsev A.V., Khismatov R.F., Shashkov S.N. Methodology for estimating the efficiency of optical-electronic systems using an analytical model.  Functions of threshold contrast and modulation transfer // information and measurement and controlling systems. 2021. Vol. 19. № 1. pp. 45-64. DOI: 10.18127/j20700814-202101-04
  8. Krasnov A.M., Tregubenkov S.Yu., Rumyantsev A.V., Khismatov R.F., Shashkov S.N. Methodology for estimating the efficiency of optical-electronic systems using an analytical model. Noise model of the «OES-operator» system. Trudy MAI, 2022, no.122. DOI: 10.34759/trd-2022-122-22
  9. J. A. D’Agostino and C. M. Webb, «Three-dimensional analysis framework and measurement methodology for imaging system noise» Proc. SPIE 1488, 110 (1991).
  10. Patrick O’Shea, Stephen Sousk “Practical Issues with 3D-Noise Measurements and Application to Modern Infrared Sensors”. Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVI, Proceedings of SPIE Vol. 5784, 2005; doi: 10.1117/12.604588.
  11. Haefner, D. P. and Burks, S., "Finite sampling corrected 3D noise with confidence intervals," Appl. Opt. 54, 4907-4915 (2015); doi:10.1364/AO.54.004907.
  12. Ronald G. Driggers, Melvin H. Friedman, Jonathan Nichols. Introduction to Infrared and Electro-Optical Systems. Second Edition. Artech House, Boston, London, 2012.
  13. Сhrzanowski K. Testing thermal imagers. Practical guidebook. // Military University of Technology, 00-908 Warsaw, Poland, 2010.
  14. Bareła J., Kastek M., Firmanty K., Trzaskawka P. Determining the range parameters of observation thermal cameras on the basis of laboratory measurements. Proc. of SPIE Vol. 8896 889610-1, doi: 10.1117/12.2028703, 2013.
  15. Corey D. Packard, Mark D. Klein, Timothy S. Viola, David C. Bell, Peter L. Rynes, "Automated simulation-generated EO/IR image library for artificial intelligence applications," Proc. SPIE 11406, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXI, 1140606 (23 April 2020); doi: 10.1117/12.2558843.
  16. Khismatov I.F. Methodology of meteorological conditions reproduction at simulation modeling of aviation optoelectronic basing systems // Trudy MAI, 2023, no. 108 https://doi.org/10.34759/trd-2019-108-18.
  17. Shipko V.V. Interference-resistant complexing of multi- and hyperspectral images in optoelectronic complexes of information support of modern and perspective helicopters // Trudy MAI, 2020, no. 110. DOI: 10.34759/trd-2020-110-12.
  18. Chernikov A.A. Algorithm for detecting and classification of objects on a unhomogeneous background for optoelectronic systems. Trudy MAI, 2023, no. 129. DOI: 10.34759/trd-2023-129-26
  19. Selvesyuk N.I., Veselov Y.G., Gaidenkov A.V., Ostrovsky A.S. Evaluation of the characteristics of detection and recognition of objects in the image from specific optical-electronic systems of observation of the airfield // Trudy MAI, 2023, no. 103. URL: http://trudymai.ru/published.php?ID=100782 
  20. N. I. Iakovleva, Estimation of the maximum distance to observation objects using a staring FPA Usp. Prikl. Fiz. 8 (5), 341 (2020).


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