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Computer Optics, 2019, Volume 43, Issue 3, Pages 402–411 (Mi co660)  

IMAGE PROCESSING, PATTERN RECOGNITION

Test-object recognition in thermal images

A. V. Mingalev, A. V. Belov, I. M. Gabdullin, R. R. Agafonova, S. N. Shusharin

JSC “Scientific and Production Association “State Institute of Applied Optics”, Kazan, Russia

Abstract: The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode. We consider methods of image recognition based on the correlation image comparison, Viola-Jones method, LeNet classificatory convolutional neural network, GoogleNet (Inception v.1) classificatory convolutional neural network, and a deep-learning-based convolutional neural network of Single-Shot Multibox Detector (SSD) VGG16 type. The best performance is reached via using the deep-learning-based convolutional neural network of the VGG16-type. The main advantages of this method include robustness to variations in the test object size; high values of accuracy and recall parameters; and doing without additional methods for RoI (region of interest) localization.

Keywords: image classification, object detection in images, image recognition, deep-learning-based convolutional neural network, thermal image, thermal imaging device.

DOI: https://doi.org/10.18287/2412-6179-2019-43-3-402-411

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Full text: http://www.computeroptics.smr.ru/.../430309.html
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Received: 17.06.2018
Accepted:17.03.2019

Citation: A. V. Mingalev, A. V. Belov, I. M. Gabdullin, R. R. Agafonova, S. N. Shusharin, “Test-object recognition in thermal images”, Computer Optics, 43:3 (2019), 402–411

Citation in format AMSBIB
\Bibitem{MinBelGab19}
\by A.~V.~Mingalev, A.~V.~Belov, I.~M.~Gabdullin, R.~R.~Agafonova, S.~N.~Shusharin
\paper Test-object recognition in thermal images
\jour Computer Optics
\yr 2019
\vol 43
\issue 3
\pages 402--411
\mathnet{http://mi.mathnet.ru/co660}
\crossref{https://doi.org/10.18287/2412-6179-2019-43-3-402-411}


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