A New Approach for Classification and Detection of World Cultural Heritages with YOLOv3
Yazarlar (2)
Arş. Gör. Mert ÇEÇEN Kastamonu Üniversitesi, Türkiye
Prof. Dr. Mehmet Karaköse Firat Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | Ingilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/ICSH57060.2023.10482838 | ||
| Kongre Adı | International Conference on Sustaining Heritage: Innovative and Digital Approaches (ICSH) | ||
| Kongre Tarihi | 18-06-2023 / 19-06-2023 | ||
| Basıldığı Ülke | Bahreyn | Basıldığı Şehir | |
| Bildiri Linki | https://doi.org/10.1109/icsh57060.2023.10482838 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Yapay Zeka
Makine Öğrenmesi
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| Özet |
| Since the formation of the world, many civilizations have lived and these civilizations have left important information about their social and architectural structures by creating a cultural heritage infrastructure in order to transfer their experiences to future generations. In this process, civilizations have left many cultural heritage works, both tangible and intangible. The concept of digital heritage has emerged for the protection and promotion of cultural heritage sites and works that still exist today, and studies have been carried out in the literature for the digitization of the works of certain regions. In this study, apart from literature studies, the images of 200 different artifacts included in the World Heritage List, which contain images of various structures from different regions created by the United Nations Educational, Scientific and Cultural Organization (UNESCO), are manually collected, labeled and classified and … |
| Anahtar Kelimeler |
| Convolutional Neural Networks | Cultural Heritage | Deep Learning | YOLOv3 |
| Atıf Sayıları | |
| Scopus | 4 |
| Google Scholar | 6 |