Published August 31, 2022 | Version v1
Journal article Open

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

Creators

  • 1. Kharkiv National University of Radio Electronics
  • 2. Ivan Kozhedub Kharkiv National Air Force University
  • 3. Academician Yuriy Bugay International Scientific and Technical University
  • 4. Military Institute of Kyiv National University Taras Shevchenko
  • 5. State Scientific Research Institute of Armament and Military Equipment Testing and Certification
  • 6. Hetman Petro Sahaidachnyi National Army Academy

Description

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.

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