Methods of UAVs images segmentation based on k-means and a genetic algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.263387

Keywords:

unmanned aerial vehicle, image segmentation, experimental research, k-means, genetic algorithm

Abstract

The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined.

An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles.

The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that:

– the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively;

– average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively.

It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.

Author Biographies

Igor Ruban, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor, First Vice-Rector

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Oleksandr Makoveichuk, Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Vladyslav Khudov, Kharkiv National University of Radio Electronics

PhD, Junior Researcher

Department of Information Technology Security

Temir Kalimulin, Ivan Kozhedub Kharkiv National Air Force University

Adjunct

Department of Radar Troops Tactic

Sergey Glukhov, Military Institute of Kyiv National University Taras Shevchenko

Doctor of Technical Sciences, Professor, Head of Department

Department of Military and Technical Training

Pavlo Arkushenko, State Scientific Research Institute of Armament and Military Equipment Testing and Certification

Head of the Research Department

Scientific and Research Department of Tests of Information and Measurement Systems and Control Complexes

Taras Kravets, Hetman Petro Sahaidachnyi National Army Academy

Lecturer

Department of Complexes and Devices of Artillery Recconaissance

Irina Khizhnyak, Ivan Kozhedub Kharkiv National Air Force University

PhD

Department of Radar Troops Tactic

Nazar Shamrai, Military Institute of Kyiv National University Taras Shevchenko

Senior Researcher

Department of Military Technical and Information Research

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Published

2022-08-31

How to Cite

Ruban, I., Khudov, H., Makoveichuk, O., Khudov, V., Kalimulin, T., Glukhov, S., Arkushenko, P., Kravets, T., Khizhnyak, I., & Shamrai, N. (2022). Methods of UAVs images segmentation based on k-means and a genetic algorithm. Eastern-European Journal of Enterprise Technologies, 4(9(118), 30–40. https://doi.org/10.15587/1729-4061.2022.263387

Issue

Vol. 4 No. 9(118) (2022): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2022 Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Vladyslav Khudov, Temir Kalimulin, Sergey Glukhov, Pavlo Arkushenko, Taras Kravets, Irina Khizhnyak, Nazar Shamrai

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