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Algorithm for the detection of detailed objects in images fixation from UAVs and analysis of its application for the selection of elements of various sizes.

Authors :
Semenishchev, Evgenii
Voronin, Viacheslav
Gapon, Nikolay
Khamidullin, Ilya
Tokareva, Olga
Gavlicky, Alexander
Balabaeva, Oxana
Source :
AIP Conference Proceedings. 2023, Vol. 2507 Issue 1, p1-6. 6p.
Publication Year :
2023

Abstract

Analysis of the data obtained from the UAV is hampered by the low performance of the calculation unit on board the device. Most often, the procedure is carried out on a remote unit, data transmission to which is carried out via a wireless communication channel. The need for such a solution is due to the requirements for the operations performed, including both the analysis and formation of the control actions of the UAV and the decision-making on changes in the current flight tasks. The paper proposes an algorithm for the automated selection of areas with great detail. The algorithm allows you to reduce the amount of analyzed data. Reducing the size of the analyzed blocks allows you to introduce parallelization of processes, reduce the requirements for computing resources, select processing parameters and increase the speed of data analysis. The proposed algorithm is based on data analysis in local windows, with an automated selection of coefficients. The test dataset shows examples of masks of detailed objects that have different sizes. The proposed algorithm detects detailed elements in images with a complex object structure from 80% to 10% from the size image. For a small object size, the block was deleted. For large sizes, the algorithm searches elements inside the object by gouging out a part of it. The proposed algorithm allowed for an increase in the speed of analysis by 24% compared to the previously developed approach and made it possible to analyze data in the infrared range. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0094243X
Volume :
2507
Issue :
1
Database :
Academic Search Index
Journal :
AIP Conference Proceedings
Publication Type :
Conference
Accession number :
164413976
Full Text :
https://doi.org/10.1063/5.0114113