Intelligent System of Generation of Camouflage Patterns Based on Artificial Intelligence Technologies

Authors

DOI:

https://doi.org/10.18372/1990-5548.80.18678

Keywords:

artificial neural networks, аrtificial Intelligence, intelligent generation system, generative-competitive network, progressively growing GANs, camouflage patterns

Abstract

The work is devoted to the development of an intelligent system for generating camouflage patterns based on artificial intelligence technologies. A generative-competitive network is used as an intellectual element of this system. To solve the problem of the collapse mode, the architecture of progressively growing GANs (ProGAN) is used. The system allows you to generate completely new camouflage patterns for the selected area by iteratively improving the pattern. Due to the mechanism of restrictions, it is possible to fix the desired aspects of the drawing (color scheme, pattern, number of colors) from an existing drawing and adapt it to the desired area. The system provides the possibility of generating micropatterns on the drawings to improve camouflage at close distances. When evaluating a camouflage pattern, the system takes into account additional parameters, such as angle (from the ground and air), time and weather.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv, Ukraine

Doctor of Engineering Science

Professor

Head of the Department of Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Dmytro Nikulin , National Aviation University, Kyiv, Ukraine

Student.

Aviation Computer Integrated Complexes Department

Faculty of Air Navigation, Electronics and Telecommunications

References

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014.

F. Chollet, Deep learning with Python. Manning Publications Co., 2017, pp. 364–380.

J. Langr and V. Bok, Generative adversarial networks (GANs) in action. Manning Publications Co., 2019, pp. 10–35.

A. Narita, K. Yoshioka, and D. J. Im, Generative adversarial networks with industrial applications. Springer, 2020.

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Published

2024-06-25

Issue

Vol. 2 No. 80 (2024)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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