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Conference Paper
Deep Learning-Based Damage Detection for an Intelligent Monitoring System for Cultural Heritage
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- Authors
- Dohun Kim, Minho Bae, Hayoung Lee, Seonghee Lee
- Issue Date
- 2025-08
- Citation
- International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2025, pp.1-7
- Language
- English
- Type
- Conference Paper
- Abstract
- Cultural heritage is essential for preserving the identity and history of civilizations, but its protection faces challenges from natural deterioration, environmental factors, and human activity. This study presents an intelligent monitoring system using an autonomous mobile robot, applied to Gongsanseong Fortress, a historic site in Korea. The system monitors structural damage at key points of interest, such as fortress walls and stone monuments, and also tracks long-term deterioration. It consists of an edge server for on-site data processing and localization, as well as a cloud server that performs deep learning-based damage detection. To assess potential collapse risks, a classification model is employed that considers factors including moisture and soil composition. Performance validation using data collected from Gongsanseong Fortress demonstrates the system's effectiveness in damage detection, environmental impact assessment, and informed decision-making for heritage conservation.
- KSP Keywords
- Autonomous mobile robots, Classification models, Cloud server, Cultural Heritage, Data collected, Data processing, Decision-making, Environmental Factors, Environmental Impact Assessment(EIA), Heritage conservation, Human activity