Development of YOLOv8 and Segment Anything Model Algorithm-Based Hanok Object Detection Model for Sustainable Maintenance of Hanok Architecture - Shin, Byeong-Uk (Sustainability, 2024)
A Hanok refers to a traditional Korean architectural structure. Construction structures undergo gradual, rather than instantaneous, transformations due to material degradation and deterioration in joint durability. Moreover, the detection of a structural problem by a nonexpert has severe implications for the safety of the structure. In particular, the precise effects of natural disasters, including storms, earthquakes, heavy snowfall, and structural defects, on structures are hard to determine. Additionally, manuals are limited by their reliance on quantitative assessments, which can pose difficulties for nonspecialists when it comes to recording numerical data. To solve this problem, 3D scanners have been widely employed in evaluating Hanoks, particularly those assigned as cultural heritage by the government. While those assigned as cultural heritage assets are systematically managed by experts and through budgets, the management system for Hanoks inhabited by the public has been overlooked. To fill this gap, this study focused on digital devices that are accessible to nonexperts as replacements for professional 3D scanners. Specifically, data from photos of a Hanok taken with smartphones were extracted to generate objective numerical data. AI training data for Hanoks were used to train the YOLOv8 algorithm and Segment Anything Model (SAM). The leaning values of columns, which constitute a fundamental structural component of a Hanok, were calculated using photographs that precisely captured the columns. The direction and distance of the column’s movement were extracted for visualization. To ensure the reliability of these values, the Hanok under investigation was 3D-scanned. Comparing the numerical values revealed a negligible margin of error, which confirmed the reliability of the photographic data values. Five-tier safety states (good, observation, caution, danger, and very dangerous) were defined based on the column movement distance by analyzing the real measurement data of government-managed Hanoks and used to visualize the structural condition of Hanoks. Therefore, nonexperts can determine the structural safety of a Hanok using objective numerical data, even in situations where its progressive deformation is not readily apparent. Objective numerical analysis based on reliably collected data allows nonexperts to accurately diagnose structural safety, thus facilitating prompt and suitable actions. The results of this study can serve to enhance the stability and longevity of Hanok structures, thus facilitating sustainable maintenance and management.