Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm
| Autor: | Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Igor Gyrenko, Vitalii Stryhun, Oleh Bilous, Nazar Shamrai, Anna Kovalenko, Irina Khizhnyak, Rostyslav Khudov |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
camouflaged military equipment
Applied Mathematics Mechanical Engineering Energy Engineering and Power Technology Industrial and Manufacturing Engineering Computer Science Applications optoelectronic image Control and Systems Engineering Management of Technology and Innovation genetic algorithm Environmental Chemistry Electrical and Electronic Engineering Food Science chromosome population |
| Popis: | The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems. The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following: –highlighting brightness channels in the Red-Green-Blue color space; –the use of a genetic algorithm in the image in each channel of brightness of the RGB color space; –image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space. Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems. A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way: –compared to the known k-means method, by an average of 15% of errors of the first kind and an average of 7% of errors of the second kind; –compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8% of errors of the first kind and an average of 2.9% of errors of the second kind. The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems |
| Databáze: | OpenAIRE |
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