Investigation of the neural networks effectiveness in recognizing moving drones

Зубков, О. В.; Шейко, С. О.; Олейніков, В. М.; Карташов, В. М.

Публікація:
Investigation of the neural networks effectiveness in recognizing moving drones

dc.contributor.author	Зубков, О. В.
dc.contributor.author	Шейко, С. О.
dc.contributor.author	Олейніков, В. М.
dc.contributor.author	Карташов, В. М.
dc.date.accessioned	2022-07-05T21:38:00Z
dc.date.available	2022-07-05T21:38:00Z
dc.date.issued	2021
dc.description.abstract	The aim of the work was to study the recognition efficiency of a moving unmanned aerial vehicle (drone) using various neural networks. Models of fully connected and convolutional neural networks had been created and trained making it possible to classify 12 types of moving objects. Sets of images, such as drones, fragments of tree foliage, grass, clouds and insects had been created to train neural networks. The results of the work are: recommendations for choosing the type and structure of the neural network, estimation of the recognition range for high-resolution images.	uk_UA
dc.identifier.citation	O. Zubkov, S. Sheiko, V. Oleynikov and V. Kartashov, "Investigation of the neural networks effectiveness in recognizing moving drones," 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), 2021, pp. 119-122, doi: 10.1109/CSIT52700.2021.9648717.	uk_UA
dc.identifier.isbn	978-166544257-2
dc.identifier.issn	27663655
dc.identifier.uri	https://ieeexplore.ieee.org/document/9648717
dc.identifier.uri	https://openarchive.nure.ua/handle/document/20658
dc.language.iso	en	uk_UA
dc.publisher	CSIT	uk_UA
dc.subject	neural network	uk_UA
dc.subject	drone	uk_UA
dc.subject	motion detection	uk_UA
dc.subject	convolutional network	uk_UA
dc.subject	video stream	uk_UA
dc.subject	dataset	uk_UA
dc.title	Investigation of the neural networks effectiveness in recognizing moving drones	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication