Analysis of Dataset of Textile Materials Macro Images to Detect Data Leaks

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dc.contributor.authorMerezhko, Ye.
dc.contributor.authorYurchenko, D.
dc.contributor.authorMolchanova, M.
dc.contributor.authorMazurets, O.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2026-05-20T11:31:35Z
dc.date.available2026-05-20T11:31:35Z
dc.date.issued2026
dc.description.abstractThe paper analyzes a dataset of textile material macro images to detect redundancy-related data leaks that may negatively affect the reliability of neural network classification. The study emphasizes that textile datasets often contain visually similar or near-duplicate images due to repetitive fabric textures and repeated image acquisition conditions, which can lead to inflated evaluation results and reduced model generalization. The proposed approach combines perceptual hashing (pHash) and deep embeddings extracted from a pretrained ResNet18 model to identify and remove redundant images from the dataset. Experimental results demonstrate that dataset cleaning improves classification stability and increases the trustworthiness of CNN-based textile recognition models. The practical significance of the work lies in improving the quality of textile datasets for computer vision tasks, automated fabric classification, and intelligent textile recycling systems.
dc.identifier.citationMerezhko Ye., Yurchenko D., Molchanova M., Mazurets O. Analysis of Dataset of Textile Materials Macro Images to Detect Data Leaks. Proceedings of IV International Scientific and Practical Conference «Science, Technology, and Industry in the Digital Age». April 22-24, 2026. Hamburg, Germany. Pp. 215-220.
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/21109
dc.language.isoen
dc.titleAnalysis of Dataset of Textile Materials Macro Images to Detect Data Leaks
dc.typeСтаття
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