Research on the effectiveness of neural network detection of plots with the destroyed buildings remains
Вантажиться...
Дата
2025
Назва журналу
Номер ISSN
Назва тому
Видавець
Анотація
This paper explores the effectiveness of neural network approaches for detecting and classifying plots containing the remains of destroyed buildings using aerial imagery. The proposed method integrates a YOLO-based object detector and a Vision Transformer for multi-class classification of structural debris such as concrete, metal, brick, and wood. The system achieves high accuracy (97%) and demonstrates strong performance across key classification metrics. This research highlights the critical role of deep learning in accelerating post-disaster damage assessment, supporting emergency response, cultural heritage preservation, and long-term urban resilience planning.
Опис
Ключові слова
Бібліографічний опис
Didur V., Molchanova M., Mazurets O. Research on the effectiveness of neural network detection of plots with the destroyed buildings remains. Modern technologies and science: problems, new and relevant developments. Proceedings XXI International Scientific and Practical Conference. May 26, 2025. Zaragoza, Spain. Pp. 245-251