Effectiveness research of using ViT neural network architecture for classifying the destroyed buildings remains

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dc.contributor.authorHladun, O.V.
dc.contributor.authorMolchanova, M.O.
dc.contributor.authorZalutska, O.O.
dc.contributor.authorMazurets, O.V.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2025-05-26T12:52:13Z
dc.date.available2025-05-26T12:52:13Z
dc.date.issued2025
dc.description.abstractThis study explores the effectiveness of the Vision Transformer (ViT) neural network for classifying remains of destroyed buildings in post-disaster environments. A software system was developed to preprocess images, train ViT and MobileNetV3 models, and integrate them into a user-friendly application. The models, trained on real-world construction debris images from robotic systems, showed high classification accuracy. Results confirm the ViT model’s potential for reliable, automated damage assessment, supporting faster and safer disaster response.
dc.identifier.citationHladun O.V., Molchanova M.O., Zalutska O.O., Mazurets O.V. Effectiveness research of using ViT neural network architecture for classifying the destroyed buildings remains. Achievements of Science and Applied Research. Proceedings 2nd International Scientific and Practical Conference. May 19-21, 2025. Dublin, Ireland. Pp. 96-100
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18385
dc.language.isoen
dc.titleEffectiveness research of using ViT neural network architecture for classifying the destroyed buildings remains
dc.typeСтаття
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