High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection
One of the key barriers to neural network adoption is the lack of computational resources and high-quality training data—particularly for unique objects without existing datasets. This research explores methods for generating realistic synthetic images that preserve the visual properties of target o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/5/120 |
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| author | Nataliya Shakhovska Bohdan Sydor Solomiia Liaskovska Olga Duran Yevgen Martyn Volodymyr Vira |
| author_facet | Nataliya Shakhovska Bohdan Sydor Solomiia Liaskovska Olga Duran Yevgen Martyn Volodymyr Vira |
| author_sort | Nataliya Shakhovska |
| collection | DOAJ |
| description | One of the key barriers to neural network adoption is the lack of computational resources and high-quality training data—particularly for unique objects without existing datasets. This research explores methods for generating realistic synthetic images that preserve the visual properties of target objects, ensuring their similarity to real-world appearance. We propose a flexible approach for synthetic data generation, focusing on improved accuracy and adaptability. Unlike many existing methods that rely heavily on specific generative models and require retraining with each new version, our method remains compatible with state-of-the-art models without high computational overhead. It is especially suited for user-defined objects, leveraging a 3D representation to preserve fine details and support integration into diverse environments. The approach also addresses resolution limitations by ensuring consistent object placement within high-quality scenes. |
| format | Article |
| id | doaj-art-e29623a383dd4f7a9b2bc63b4c7df08c |
| institution | DOAJ |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-e29623a383dd4f7a9b2bc63b4c7df08c2025-08-20T03:14:41ZengMDPI AGComputation2079-31972025-05-0113512010.3390/computation13050120High-Fidelity Synthetic Data Generation Framework for Unique Objects DetectionNataliya Shakhovska0Bohdan Sydor1Solomiia Liaskovska2Olga Duran3Yevgen Martyn4Volodymyr Vira5Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, UkraineDepartment of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, UkraineDepartment of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, UkraineDepartment of Mechanical Engineering, Faculty of Engineering, Computing and the Environment, Kingston University, Room RV MB 215, Main Building (RV), Roehampton Vale, Kingston, London KT12EE, UKDepartment of Project Management, Information Technologies and Telecommunication, Lviv State University of Life Safety, 79007 Lviv, UkraineDepartment of Strength of Materials and Structural Mechanics, Lviv Polytechnic National University, 6 Starosolskykh Street, 79013 Lviv, UkraineOne of the key barriers to neural network adoption is the lack of computational resources and high-quality training data—particularly for unique objects without existing datasets. This research explores methods for generating realistic synthetic images that preserve the visual properties of target objects, ensuring their similarity to real-world appearance. We propose a flexible approach for synthetic data generation, focusing on improved accuracy and adaptability. Unlike many existing methods that rely heavily on specific generative models and require retraining with each new version, our method remains compatible with state-of-the-art models without high computational overhead. It is especially suited for user-defined objects, leveraging a 3D representation to preserve fine details and support integration into diverse environments. The approach also addresses resolution limitations by ensuring consistent object placement within high-quality scenes.https://www.mdpi.com/2079-3197/13/5/120neural networksdiffusion generative modelssynthetic training data3D reconstruction |
| spellingShingle | Nataliya Shakhovska Bohdan Sydor Solomiia Liaskovska Olga Duran Yevgen Martyn Volodymyr Vira High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection Computation neural networks diffusion generative models synthetic training data 3D reconstruction |
| title | High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection |
| title_full | High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection |
| title_fullStr | High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection |
| title_full_unstemmed | High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection |
| title_short | High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection |
| title_sort | high fidelity synthetic data generation framework for unique objects detection |
| topic | neural networks diffusion generative models synthetic training data 3D reconstruction |
| url | https://www.mdpi.com/2079-3197/13/5/120 |
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