{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:32:25Z","timestamp":1781281945482,"version":"3.54.1"},"reference-count":57,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe research and innovation program","award":["101138678"],"award-info":[{"award-number":["101138678"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>One of the key barriers to neural network adoption is the lack of computational resources and high-quality training data\u2014particularly 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.<\/jats:p>","DOI":"10.3390\/computation13050120","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:44:58Z","timestamp":1747212298000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-8534","authenticated-orcid":false,"given":"Nataliya","family":"Shakhovska","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohdan","family":"Sydor","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0822-0951","authenticated-orcid":false,"given":"Solomiia","family":"Liaskovska","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, Kniazia Romana Street, 5, 79005 Lviv, Ukraine"},{"name":"Department of Mechanical Engineering, Faculty of Engineering, Computing and the Environment, Kingston University, Room RV MB 215, Main Building (RV), Roehampton Vale, Kingston, London KT12EE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Duran","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, Computing and the Environment, Kingston University, Room RV MB 215, Main Building (RV), Roehampton Vale, Kingston, London KT12EE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yevgen","family":"Martyn","sequence":"additional","affiliation":[{"name":"Department of Project Management, Information Technologies and Telecommunication, Lviv State University of Life Safety, 79007 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5121-7336","authenticated-orcid":false,"given":"Volodymyr","family":"Vira","sequence":"additional","affiliation":[{"name":"Department of Strength of Materials and Structural Mechanics, Lviv Polytechnic National University, 6 Starosolskykh Street, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ye, Y., Ma, X., Zhou, X., Bao, G., Wan, W., and Cai, S. 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