Development of an image segmentation method from unmanned aerial vehicles based on the particle swarm optimization algorithm

Authors

DOI:

https://doi.org/10.15587/2706-5448.2025.330973

Keywords:

segmentation, unmanned aerial vehicle, swarm intelligence, particle swarm algorithm, k-means

Abstract

The object of research is the process of segmenting images from an unmanned aerial vehicle based on the particle swarm algorithm.

One of the most problematic areas in segmenting images from unmanned aerial vehicles is the reduction in the efficiency of known segmentation methods. In addition, most methods do not accurately recognize small objects that occupy a small part of the image.

The method of segmenting images from an unmanned aerial vehicle based on the particle swarm algorithm has been improved, in which, unlike the known ones, the following is performed:

– the source image is converted to the appropriate color space;

– the channel is selected for further analysis;

– the particle swarm is initialized on the source image in each channel selected for further analysis;

– the objective function is calculated for each particle of the swarm in the image in each selected channel;

– the current value of the objective function for each particle of the swarm is compared with the best value of the objective function in the image in each selected channel;

– calculating the velocity value and new location for each swarm particle in the image;

– moving each swarm particle in the image in each selected channel;

– determining the swarm particles with the best value of the objective function in the image in each channel;

– combining the channels and forming the resulting image.

During the study, it was found that the segmented image by the improved method based on the particle swarm algorithm has better visual quality compared to the known segmentation method. It was found that the improved segmentation method based on the particle swarm algorithm provides an average reduction in segmentation errors of the I kind by 11% and an average reduction in segmentation errors of the II kind by 9%.

Supporting Agency

  • The research was conducted with the grant support of the National Research Foundation of Ukraine within the framework of the competition “Science for Strengthening the Defense Capability of Ukraine”, “Information Technology for Segmenting Object Images in FPV Drone Targeting Systems Based on Swarm Intelligence Algorithms” project, registration number 2023.04/0153.

Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Vladyslav Khudov, Kharkiv National University of Radio Electronics

PhD, Junior Researcher

Department of Information Technology Security

Oleksandr Makoveichuk, Higer Educational Institution Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Irina Khizhnyak, Ivan Kozhedub Kharkiv National Air Force University

Scientific and Methodological Department for Quality Assurance in Educational Activities and Higher Education

Illia Hridasov, Ivan Kozhedub Kharkiv National Air Force University

Leading Researcher

Scientific and Methodical Department

Ihor Butko, Higer Educational Institution Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Professor

Department of Computer Sciences and Software Engineering

Rostyslav Khudov, V. N. Karazin Kharkiv National University

Department of Theoretical and Applied Informatics

Sergey Glukhov, Military Institute of National Taras Shevchenko University of Kyiv

Doctor of Technical Sciences, Professor

Department of Military and Technical Training

Nazar Shamrai, Military Institute of National Taras Shevchenko University of Kyiv

Head of Department

Department of Military Technical and Information Research

Bohdan Lisohorskyi, Ivan Kozhedub Kharkiv National Air Force University

PhD, Senior Researcher

Department of Radar Troops Tactic

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Development of an image segmentation method from unmanned aerial vehicles based on the particle swarm optimization algorithm

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Published

2025-05-29

How to Cite

Khudov, H., Khudov, V., Makoveichuk, O., Khizhnyak, I., Hridasov, I., Butko, I., Khudov, R., Glukhov, S., Shamrai, N., & Lisohorskyi, B. (2025). Development of an image segmentation method from unmanned aerial vehicles based on the particle swarm optimization algorithm. Technology Audit and Production Reserves, 3(2(83), 88–95. https://doi.org/10.15587/2706-5448.2025.330973

Issue

Vol. 3 No. 2(83) (2025): Information and control systems

Section

Systems and Control Processes

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Copyright (c) 2025 Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Illia Hridasov, Ihor Butko, Rostyslav Khudov, Sergey Glukhov, Nazar Shamrai, Bohdan Lisohorskyi

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