Informacionnye TehnologiiInformacionnye TehnologiiИнформационные технологии1684-6400New Technologies Publishing House71649610.17587/it.32.382-392Digital processing of signals and imagesЦифровая обработка сигналов и изображенийResearch ArticleApplication of neural network methods in the synthesis of sonar imagesПрименение нейросетевых методов при синтезе гидролокационных изображенийMironovA. S.МироновА. С.
Russian Federation

Cand. Of Tech. Sc., Assistant Professor

канд. техн. наук, доц.

andrei.s.mironov@yandex.ruSaenkoA. A.СаенкоА. А.
Russian Federation

Postgraduate Student, Assistant lecturer

аспирант, ассистент

saa2410@mail.ruFominaE. S.ФоминаЕ. С.
Russian Federation

Senior Lecturer

старший преподаватель

fominaekt@gmail.comPacific National UniversityТихоокеанский государственный университет170720263273823921707202617072026Copyright ©; 2026, Informacionnye TehnologiiCopyright ©; 2026, Информационные технологии2026Informacionnye TehnologiiИнформационные технологииhttps://journals.eco-vector.com/1684-6400/article/view/716496

The study examines the effectiveness of neural network-based methods for the synthesis of sonar images. An experimental evaluation was conducted to assess the performance of the neural style transfer method, the deep painterly harmonization method, and the arbitrary style transfer method with adaptive normalization. An original modification of the neural style transfer method is proposed, which demonstrated superior effectiveness compared to other methods in generating sonar images.

Рассматриваются вопросы эффективности применения нейросетевых методов синтеза гидролокационных изображений. Была проведена экспериментальная оценка эффективности применения метода нейросетевой передачи стиля, метода глубокой живописной гармонизации и метода передачи произвольного стиля с адаптивной нормализацией. Предложена авторская модификация метода нейросетевой передачи стиля, которая показала свою эффективность по сравнению с другими методами при генерации гидролокационных изображений.

neural style transfer methodsimage embeddingartificial neural networkshydroacoustic computer vision systemsнейросетевые методы передачи стилявнедрение изображенийискусственные нейронные сетигидроакустические системы технического зренияРоссийский научный фондRussian Science Foundation24-11-20024The research was supported financially by the Russian Science Foundation within the scientific project no.: 24-11-20024.Исследование выполнено за счет гранта Российского научного фонда № 24-11-20024 "Методы и средства построения цифровых моделей русла, береговой линии и водоохранной зоны реки Амур и ее основных притоков".Lavagnino A. C., Leite M. D., Franco T., Menandro P. S., Vieira F. V., Boni G. C., Bastos А. C. Seabed Acoustic Mapping Revealing an Uncharted Habitat of Circular Depressions Along the Southeast Brazilian Outer Shelf, Geosciences, 2025, vol. 15, no. 1, pp. 7.Lavagnino A. C., Leite M. D., Franco T., Menandro P. S., Vieira F. V., Boni G. C., Bastos А. C. Seabed Acoustic Mapping Revealing an Uncharted Habitat of Circular Depressions Along the Southeast Brazilian Outer Shelf // Geosciences. 2025. Vol. 15, N. 1. P. 7.Li J., Zhang G., Jiang C., Zhang W. А survey of maritime unmanned search system: Theory, applications and future directions, Ocean Engineering, 2023, vol. 285, pp. 115359.Li J., Zhang G., Jiang C., Zhang W. А survey of maritime unmanned search system: Theory, applications and future directions // Ocean Engineering. 2023. Vol. 285. P. 115359.Janowski L., Kubacka M., Pydyn A., Popek M., Gajewski L. From acoustics to underwater archaeology: Deep investigation of a shallow lake using high-resolution hydroacoustics — The case of Lake Lednica, Poland, Archaeometry, 2021, vol. 63, no. 5, pp. 1059—1080.Janowski L., Kubacka M., Pydyn A., Popek M., Gajewski L. From acoustics to underwater archaeology: Deep investigation of a shallow lake using high-resolution hydroacoustics — The case of Lake Lednica, Poland // Archaeometry. 2021. Vol. 63, N. 5. P. 1059—1080.Bertrand A., Bertrand S., Assunção R., Docean A. S., Vargas G., Lucena-Frédou F., Travassos P., Lebourges-Dhaussy A., Roudaut G. Underwater acoustics for ecosystem research: Synthesis of current knowledge and current advances and perspectives in Northeast Brazil, 2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics). Rio de Janeiro, Brazil, 2017, pp. 1—4.Bertrand A., Bertrand S., Assunção R., Docean A. S., Vargas G., Lucena-Frédou F., Travassos P., Lebourges-Dhaussy A., Roudaut G. Underwater acoustics for ecosystem research: Synthesis of current knowledge and current advances and perspectives in Northeast Brazil // 2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics). Rio de Janeiro, Brazil. 2017. P. 1—4.Vignesh U., Parvathi R., Kumar P. S., Al-Obaidi A. S. А Comparative Study on Technologies Deployed for Marine Ecosystem Monitoring and Restoration, Technological Advancements for Deep Sea Ecosystem Conservation and Exploration / edited by Vignesh U., Parvathi R. Hershey, PA: IGI Global, 2025, pp. 1—16.Vignesh U., Parvathi R., Kumar P. S., Al-Obaidi A. S. А Comparative Study on Technologies Deployed for Marine Ecosystem Monitoring and Restoration // Technological Advancements for Deep Sea Ecosystem Conservation and Exploration / Edited by Vignesh U., Parvathi R. Hershey, PA: IGI Global, 2025. P. 1—16.Pailhas Y., Petillot Y., Capus C. High-Resolution Sonars: What Resolution Do We Need for Target Recognition? EURASIP Journal on Advances in Signal Processing, 2010, vol. 2010, pp. 205095.Pailhas Y., Petillot Y., Capus C. High-Resolution Sonars: What Resolution Do We Need for Target Recognition? // EURASIP Journal on Advances in Signal Processing. 2010. Vol. 2010. P. 205095.Tyler A., Hunter P., Spyrakos E., Groom S., Constantinescu A., Fletcher J. Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters, Science of The Total Environment, 2016, vol. 572, pp. 1307—1321.Tyler A., Hunter P., Spyrakos E., Groom S., Constantinescu A., Fletcher J. Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters // Science of The Total Environment. 2016. Vol. 572. P. 1307—1321.Muthuvel P., Maurya S., Sudhakar T. Development of Autonomous Underwater Profiling Drifter (AUPD) and field results, Journal of Earth System Science, 2023, vol. 132, no. 1, pp. 11.Muthuvel P., Maurya S., Sudhakar T. Development of Autonomous Underwater Profiling Drifter (AUPD) and field results // Journal of Earth System Science. 2023. Vol. 132, N. 1. P. 11.Celik T., Tjahjadi T. А Novel Method for Sidescan Sonar Image Segmentation, IEEE Journal of Oceanic Engineering, 2011, vol. 36, no. 2, pp. 186—194.Celik T., Tjahjadi T. А Novel Method for Sidescan Sonar Image Segmentation // IEEE Journal of Oceanic Engineering. 2011. Vol. 36, N. 2. P. 186—194.Saenko A., Mironov A., Fomina E. Methods for Improving the Efficiency of Object Detection in Sonar Images, 2024 International Russian Automation Conference (RusAutoCon). Russia, Sochi, 2024, pp. 578—582.Saenko A., Mironov A., Fomina E. Methods for Improving the Efficiency of Object Detection in Sonar Images // 2024 International Russian Automation Conference (RusAutoCon). Russia, Sochi, 2024. P. 578—582.Gatys L., Ecker A., Bethge M. А Neural Algorithm of Artistic Style Journal of Vision, 2016, vol. 16, pp. 326.Gatys L., Ecker A., Bethge M. А Neural Algorithm of Artistic Style // Journal of Vision. 2016. Vol. 16. P. 326.Luan F., Paris S., Shechtman E., Bala K. Deep Painterly Harmonization, Computer Graphics Forum, 2018, vol. 37, no. 4, pp. 95—106.Luan F., Paris S., Shechtman E., Bala K. Deep Painterly Harmonization // Computer Graphics Forum. 2018. Vol. 37, N. 4. P. 95—106.Huang X., Belongie S. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization, 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy, 2017, pp. 1510—1519.Huang X., Belongie S. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization // 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. 2017. P. 1510—1519.Kumari S., Kumar K. Enhancing image style transfer for real-time indoor geometric data using GAN, Journal of Autonomous Intelligence, 2024, vol. 7, no. 3, pp. 1—12.Kumari S., Kumar K. Enhancing image style transfer for real-time indoor geometric data using GAN // Journal of Autonomous Intelligence. 2024. Vol. 7, N. 3. P. 1—12.Saenko A., Mironov A., Fomina E. Methods of Introducing Reference Objects Into Images Obtained Using Hydroacoustic Systems of Computer Vision, 2024 International Russian Automation Conference (RusAutoCon). Russia, Sochi, 2024, pp. 319—324.Saenko A., Mironov A., Fomina E. Methods of Introducing Reference Objects Into Images Obtained Using Hydroacoustic Systems of Computer Vision. // 2024 International Russian Automation Conference (RusAutoCon). Russia, Sochi. 2024. P. 319—324.Fomina E. S., Mironov А. S. Modeling the Process of Performing a Sonar Survey Using a Multi-Beam Echo Sounder, Systems of Control, Communication and Security, 2020, no. 3, pp. 184—202.Fomina E. S., Mironov А. S. Modeling the Process of Performing a Sonar Survey Using a Multi-Beam Echo Sounder // Systems of Control, Communication and Security. 2020. N. 3. P. 184—202.