Research on the effectiveness of classifying the remains of destroyed buildings using MobileNetV3 neural network architecture

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dc.contributor.authorHladun, O.
dc.contributor.authorZalutska, O.
dc.contributor.authorKlimenko, V.
dc.contributor.authorMazurets, O.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2025-05-26T12:53:23Z
dc.date.available2025-05-26T12:53:23Z
dc.date.issued2025
dc.description.abstractThe study investigates the effectiveness of the MobileNetV3 neural network architecture in classifying the remains of destroyed buildings, a task of increasing relevance due to the widespread destruction of infrastructure caused by military actions, natural disasters, and industrial accidents. A software application with a graphical interface was developed to enable interactive analysis of photo data using pre-trained models. The system allows users to classify construction debris into multiple categories with high accuracy and reliability. Experimental results demonstrated strong performance metrics, including an overall accuracy of 95% and high values for precision, recall, and F1-score across ten classes of construction materials. The model showed excellent discriminative capability, as evidenced by ROC curves with AUC values close to 1.00. The solution holds promise for practical applications such as automated sorting of construction waste and monitoring of damage zones, contributing to more efficient disaster response and recovery operations.
dc.identifier.citationHladun O., Zalutska O., Klimenko V., Mazurets O. Research on the effectiveness of classifying the remains of destroyed buildings using MobileNetV3 neural network architecture. Innovations in Science: From Theoretical Foundations to Practical Impact. Proceedings 1st International Scientific and Practical Conference. May 12-14, 2025. Antwerp, Belgium. Pp. 158-162
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18386
dc.language.isoen
dc.titleResearch on the effectiveness of classifying the remains of destroyed buildings using MobileNetV3 neural network architecture
dc.typeСтаття
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