AUTOMATIC SURFACE DAMAGE CLASSIFICATION DEVELOPED BASED ON DEEP LEARNING FOR WOODEN ARCHITECTURAL HERITAGE

Abstract. In this paper, we propose a system that automatically classifies the surface damages of wooden architectural cultural heritage based on deep learning algorithms. Commonly, on-site surface damage inspections of cultural heritage are carried out manually by field experts. However, it is difficult to manage cultural heritage because experts are not always onsite to check for damage. To overcome this problem, a deep-learning-based classification method is designed to detect surface damage automatically so that cultural heritage monitoring can be done in real time. The dataset required for the development of the deep learning model utilized 4,000 images taken directly from cultural heritage sites. As a result of a comparative analysis of the performance of four deep learning models for several examples of wooden architectural heritage, the damage detection rate of the deep learning model built in this study showed excellent performance between 94.00 and 96.50%. When gradient-weighted class activation mapping is applied to visualize the damage detection results, the performance of the model with the best performance stood out. The results of this paper are significant as a basic study of the development of a real-time remote damage detection system applicable to cultural heritage sites.