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Investigation of the applicability of the convolutional neural network U-Net to a problem of segmentation of aircraft images
D.A. Gavrilov 1,2

Lebedev Institute of Precise Mechanics and Computer Engineering, Russian Academy of Sciences,
Russian Federation, Moscow, 51, Leninskiy boulevard, 119991,
Moscow Institute of Physics and Technology,
Russian Federation, 9 Institutskiy per., Dolgoprudny, Moscow Region, 141701

 PDF, 1365 kB

DOI: 10.18287/2412-6179-CO-804

Pages: 575-579.

Full text of article: Russian language.

The paper investigates the applicability of the convolutional neural network "U-Net" to a problem of segmentation of aircraft images. The neural network image segmentation method is based on the "Carvana" implementation with the "U-Net" architecture. For orientation recognition, a neural network built in the Keras open neural network library based on the pretrained VGG16 neural network is used. The approach considered allows the image segmentation to be conducted. The results of the experiments have shown the possibility of a fairly accurate selection of the object of interest. The resulting binary masks make it possible to visually classify the aircraft in the image.

technical vision, detection, localization, neural network, recognition, image processing.

Gavrilov DA. Investigation of the applicability of the convolutional neural network U-Net to a problem of segmentation of aircraft images. Computer Optics 2021; 45(4): 575-579. DOI: 10.18287/2412-6179-CO-804.


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