{"id":12948,"date":"2022-07-01T12:17:43","date_gmt":"2022-07-01T09:17:43","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=12948"},"modified":"2023-02-01T23:17:23","modified_gmt":"2023-02-01T21:17:23","slug":"oslidzhennya-efektyvnosti-dvokanalnoyi-optyko-elektronnoyi-systemy-sposterezhennya-z-nejromerezhevym-kompleksuvannyam-informacziyi","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=12948","title":{"rendered":"\u0414\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 \u0434\u0432\u043e\u043a\u0430\u043d\u0430\u043b\u044c\u043d\u043e\u0457 \u043e\u043f\u0442\u0438\u043a\u043e-\u0435\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u043e\u0457 \u0441\u0438\u0441\u0442\u0435\u043c\u0438 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0437 \u043d\u0435\u0439\u0440\u043e\u043c\u0435\u0440\u0435\u0436\u0435\u0432\u0438\u043c \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0457"},"content":{"rendered":"

<\/p>\n

\u0414\u041e\u0421\u041b\u0406\u0414\u0416\u0415\u041d\u041d\u042f \u0415\u0424\u0415\u041a\u0422\u0418\u0412\u041d\u041e\u0421\u0422\u0406 \u0414\u0412\u041e\u041a\u0410\u041d\u0410\u041b\u042c\u041d\u041e\u0407 \u041e\u041f\u0422\u0418\u041a\u041e-\u0415\u041b\u0415\u041a\u0422\u0420\u041e\u041d\u041d\u041e\u0407 \u0421\u0418\u0421\u0422\u0415\u041c\u0418 \u0421\u041f\u041e\u0421\u0422\u0415\u0420\u0415\u0416\u0415\u041d\u041d\u042f \u0417 \u041d\u0415\u0419\u0420\u041e\u041c\u0415\u0420\u0415\u0416\u0415\u0412\u0418\u041c \u041a\u041e\u041c\u041f\u041b\u0415\u041a\u0421\u0423\u0412\u0410\u041d\u041d\u042f\u041c \u0406\u041d\u0424\u041e\u0420\u041c\u0410\u0426\u0406\u0407<\/p>\n

PERFORMANCE EVALUATION OF DUAL CHANNEL OPTOELECTRONIC SURVEILLANCE SYSTEM WITH NEURAL NETWORK INFORMATION FUSION<\/p>\n

\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438:\u00a0<\/strong>229-<\/strong>232<\/strong>. \u041d\u043e\u043c\u0435\u0440: \u21163, 2022 (309)\u00a0<\/strong> \"\"<\/a>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u041c\u0410\u041c\u0423\u0422\u0410 \u041c.\u0421.
\n\u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u0423\u043a\u0440\u0430\u0457\u043d\u0438 \u00ab\u041a\u0438\u0457\u0432\u0441\u044c\u043a\u0438\u0439 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u0447\u043d\u0438\u0439 \u0456\u043d\u0441\u0442\u0438\u0442\u0443\u0442 \u0456\u043c\u0435\u043d\u0456 \u0406. \u0421\u0456\u043a\u043e\u0440\u0441\u044c\u043a\u043e\u0433\u043e\u00bb
\nhttps:\/\/orcid.org\/0000-0002-7674-4984<\/a>
\ne-mail: rybalkomaryna@gmail.com<\/a>
\n\u041c\u0410\u041c\u0423\u0422\u0410 \u041e.\u0414.
\n\u0406\u043d\u0441\u0442\u0438\u0442\u0443\u0442 \u0424\u0456\u0437\u0438\u043a\u0438 \u041d\u0410\u041d \u0423\u043a\u0440\u0430\u0457\u043d\u0438
\nhttps:\/\/orcid.org\/0000-0002-6404-5879<\/a>
\ne-mail: mamuta.aleksandr@gmail.com<\/a>
\nMaryna MAMUTA
\nNational Technical University of Ukraine \u201cIgor Sikorsky Kyiv Polytechnic Institute\u201d
\nOleksandr MAMUTA
\nInstitute of Physics, National Academy of Sciences of Ukraine
\nDOI:<\/strong>\u00a0https:\/\/www.doi.org\/10.31891\/2307-5732-2022-309-3-229-232<\/a><\/p>\n

\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n

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\n\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u043e\u043f\u0442\u0438\u043a\u043e-\u0435\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u0443\u0432\u0430\u043d\u043d\u044f, \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0456 \u043c\u0435\u0440\u0435\u0436\u0456<\/p>\n

\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u00a0\u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

Optoelectronic surveillance systems are widely used in many areas of human activity: in agriculture, medicine, military systems, in search and rescue operations. These systems are designed for space, airborne, ground and maritime applications. They have to work under all climate conditions with absolute certainty under all light and weather conditions. And on the other hand, they have to be cost effective. That\u2019s why optoelectronic surveillance systems use channels which provide complimentary information about the object and background. And for the most applications channels that operate in visible and thermal ranges of optical spectrum are indispensable element of such systems. Optoelectronic surveillance systems have to continuously meet and even exceed today\u2019s performance and reliability requirements. That\u2019s why there is a need to use not only cutting-age technology, but also adaptive signal processing. Information fusion is state-of-the-art technic to improve overall system performance. Numerous image fusion methods have been proposed during several decades but the most promising are neural networks.
\nTelevision system, which work in visible range of optical spectrum, and thermal system, which work in long-wave infrared range where chosen for the modeling. Probability of target detection, recognition and identification was used for performance evaluation. Probability of target detection, recognition and identification for separate long-wave infrared and television channels were modeled. Also, probabilities were estimated for the fused data. Information fusion was done with the help of convolutional neural networks. Simulation results showed that probability of target detection, recognition and identification are almost for 6% higher for fused data compared to separate channels.
\nKeywords:<\/strong> optoelectronic surveillance system, fusion, neural network<\/p>\n

\u041b\u0456\u0442\u0435\u0440\u0430\u0442\u0443\u0440\u0430<\/strong><\/p>\n

    \n
  1. Liu Yu. Deep learning for pixel-level image fusion: Recent advances and future prospects \/ Yu Liu, Xun Chen, Zengfu Wang, Z. Jane Wang, Rabab K. Ward, Xuesong Wang \/\/ Information Fusion. \u2013 2018. \u2013 Vol. 42. \u2013 P. 158-173. \u2013 DOI: https:\/\/doi.org\/10.1016\/j.inffus.2017.10.007.<\/li>\n
  2. Ma Jiayi. Infrared and visible image fusion methods and applications: A survey \/ Jiayi Ma, Yong Ma, Chang Li \/\/ Information Fusion. \u2013 2019. \u2013 Vol 45. \u2013 P. 153-178. \u2013 DOI: https:\/\/doi.org\/10.1016\/j.inffus.2018.02.004.<\/li>\n
  3. Vollmerhausen R.H. Analysis and evaluation of sampled imaging systems \/ R.H. Vollmerhausen, D. Reago, R.G. Driggers. \u2013 Washington: SPIE Press, 2010. \u2013 304 p.<\/li>\n
  4. \u041a\u043e\u043b\u043e\u0431\u0440\u043e\u0434\u043e\u0432 \u0412. \u0413. \u041e\u0446\u0456\u043d\u043a\u0430 \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 \u0431\u0430\u0433\u0430\u0442\u043e\u043a\u0430\u043d\u0430\u043b\u044c\u043d\u0438\u0445 \u043e\u043f\u0442\u0438\u043a\u043e-\u0435\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u0438\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f \u0437 \u043a\u043e\u043c\u043f\u043b\u0435\u043a\u0441\u0443\u0432\u0430\u043d\u043d\u044f\u043c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u0457 \/ \u0412. \u0413. \u041a\u043e\u043b\u043e\u0431\u0440\u043e\u0434\u043e\u0432, \u0412. \u0406. \u041c\u0438\u043a\u0438\u0442\u0435\u043d\u043a\u043e, \u041c. \u0421. \u041c\u0430\u043c\u0443\u0442\u0430 \/\/ \u041d\u0430\u0443\u043a\u043e\u0432\u0456 \u0432\u0456\u0441\u0442\u0456 \u041d\u0422\u0423\u0423 \u201c\u041a\u041f\u0406\u201d. \u2013 2012. \u2013 \u2116 6. \u2013 C. 127\u2013131.<\/li>\n
  5. Holst G. C. Electro-optical imaging system performance \/ Gerald C. Holst. \u2013 [5th ed.]. \u2013 Winter Park, Florida: JCD Publishing, 2008. \u2013 438 p. \u2013 ISBN 978-0964000063.<\/li>\n
  6. Raj A. Handbook of research on deep learning-based image analysis under constrained and unconstrained environments \/ Alex Noel Joseph Raj, Vijayalakshmi G. V. Mahesh, Ruban Nersisson. \u2013 Hershey, PA: Engineering Science Reference, 2021. \u2013 381 p. \u2013 ISBN 9781799866923.<\/li>\n
  7. Goodfellow I. Deep learning \/ Ian Goodfellow, Yoshua Bengio, Aaron Courville. \u2013 Cambridge, MA: MIT Press, 2017. \u2013 800 p. \u2013 ISBN 978-0262035613.<\/li>\n
  8. STANAG 4347: NATO standardization agreement: definition of nominal static range performance for thermal imaging systems. \u2013 1995. \u2013 14p.<\/li>\n
  9. TAU 2. \u041f\u0440\u043e\u0441\u043f\u0435\u043a\u0442 \u0444\u0456\u0440\u043c\u0438 Teledyne FLIR, \u0421\u0428\u0410. [\u0415\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u0438\u0439 \u0440\u0435\u0441\u0443\u0440\u0441]. \u2013 \u0420\u0435\u0436\u0438\u043c \u0434\u043e\u0441\u0442\u0443\u043f\u0443: https:\/\/www.flir.com\/products\/tau-2\/<\/li>\n
  10. Blackfly S. \u041f\u0440\u043e\u0441\u043f\u0435\u043a\u0442 \u0444\u0456\u0440\u043c\u0438 Teledyne FLIR, \u0421\u0428\u0410. [\u0415\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u0438\u0439 \u0440\u0435\u0441\u0443\u0440\u0441]. \u2013 \u0420\u0435\u0436\u0438\u043c \u0434\u043e\u0441\u0442\u0443\u043f\u0443: https:\/\/www.flir.com\/products\/blackfly-s-usb3\/<\/li>\n<\/ol>\n

    References<\/strong><\/p>\n

      \n
    1. Liu Yu. Deep learning for pixel-level image fusion: Recent advances and future prospects \/ Yu Liu, Xun Chen, Zengfu Wang, Z. Jane Wang, Rabab K. Ward, Xuesong Wang \/\/ Information Fusion. \u2013 2018. \u2013 Vol. 42. \u2013 P. 158-173. \u2013 DOI: https:\/\/doi.org\/10.1016\/j.inffus.2017.10.007.<\/li>\n
    2. Ma Jiayi. Infrared and visible image fusion methods and applications: A survey \/ Jiayi Ma, Yong Ma, Chang Li \/\/ Information Fusion. \u2013 2019. \u2013 Vol 45. \u2013 P. 153-178. \u2013 DOI: https:\/\/doi.org\/10.1016\/j.inffus.2018.02.004.<\/li>\n
    3. Vollmerhausen R.H. Analysis and evaluation of sampled imaging systems \/ R.H. Vollmerhausen, D. Reago, R.G. Driggers. \u2013 Washington: SPIE Press, 2010. \u2013 304 p.<\/li>\n
    4. Kolobrodov V. G. Otsinka efektyvnosti bahatokanalnykh optyko-elektronnykh system sposterezhennia z kompleksuvanniam informatsii \/ V. G. Kolobrodov, V. I. Mykytenko, M. S. Mamuta \/\/ Naukovi visti NTUU \u201cKPI\u201d\u201d. \u2013 2012. \u2013 \u2116 6. \u2013 S. 127\u2013131.<\/li>\n
    5. Holst G. C. Electro-optical imaging system performance \/ Gerald C. Holst. \u2013 [5th ed.]. \u2013 Winter Park, Florida: JCD Publishing, 2008. \u2013 438 p. \u2013 ISBN 978-0964000063.<\/li>\n
    6. Raj A. Handbook of research on deep learning-based image analysis under constrained and unconstrained environments \/ Alex Noel Joseph Raj, Vijayalakshmi G. V. Mahesh, Ruban Nersisson. \u2013 Hershey, PA: Engineering Science Reference, 2021. \u2013 381 p. \u2013 ISBN 9781799866923.<\/li>\n
    7. Goodfellow I. Deep learning \/ Ian Goodfellow, Yoshua Bengio, Aaron Courville. \u2013 Cambridge, MA: MIT Press, 2017. \u2013 800 p. \u2013 ISBN 978-0262035613.<\/li>\n
    8. STANAG 4347: NATO standardization agreement: definition of nominal static range performance for thermal imaging systems. \u2013 1995. \u2013 14p.<\/li>\n
    9. TAU 2. Prospekt firmy Teledyne FLIR, SShA. [Elektronnyi resurs]. \u2013 Rezhym dostupu: https:\/\/www.flir.com\/products\/tau-2\/<\/li>\n
    10. Blackfly S. Prospekt firmy Teledyne FLIR, SShA. [Elektronnyi resurs]. \u2013 Rezhym dostupu: https:\/\/www.flir.com\/products\/blackfly-s-usb3\/<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[66],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/12948"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12948"}],"version-history":[{"count":4,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/12948\/revisions"}],"predecessor-version":[{"id":15745,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/12948\/revisions\/15745"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12948"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12948"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12948"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}