Devising a segmentation method for optoelectronic imagery from unmanned aerial vehicles based on the artificial bee colony algorithm
DOI:
https://doi.org/10.15587/1729-4061.2025.337170Keywords:
segmentation, optoelectronic imagery, artificial bee colony algorithm, unmanned aerial vehicleAbstract
This paper considers the process of segmenting an optoelectronic image acquired from an unmanned aerial vehicle based on the artificial bee colony algorithm. The principal hypothesis of this study assumes that the use of the artificial bee colony algorithm for segmenting an optoelectronic image acquired from an unmanned aerial vehicle could reduce segmentation errors of the first and second kinds.
A method for segmenting an optoelectronic image acquired from an unmanned aerial vehicle based on the artificial bee colony algorithm has been improved, which, unlike known ones, involves the following:
– initialization of the population of scout bees;
– calculation of the objective function;
– determining the best and promising positions;
– calculation of the optimal value of the segmentation threshold;
– image division into segments;
– checking the stopping criterion;
– bee migration;
– acquisition of a segmented image.
Experimental studies have been conducted on the segmentation of an optoelectronic image acquired from an unmanned aerial vehicle using a method based on the artificial bee colony algorithm. The visual quality of the segmented image makes it possible to conclude that segmentation using the artificial bee colony method is possible. Comparative analysis of segmented images (improved and known methods) indicates a clearer separation of the object of interest (car) using the method based on the artificial bee colony algorithm. The results of calculating segmentation errors of the first and second kind indicate a reduction in segmentation errors of the first kind by 9% and errors of the second kind by 7% when segmenting an optoelectronic image using the method based on the artificial bee colony algorithm
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Copyright (c) 2025 Hennadii Khudov, Vladyslav Khudov, Oleksandr Makoveichuk, Serhii Yarosh, Irina Khizhnyak, Valerii Varvarov, Ihor Butko, Rostyslav Khudov, Yurii Sheviakov, Artem Irkha
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