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학술대회 Displacement Detection of Wooden Cultural Properties Using Unsupervised Learning
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저자
박정우, 이상윤
발행일
202210
출처
International Conference on Consumer Electronics (ICCE) 2022 : Asia, pp.302-305
DOI
https://dx.doi.org/10.1109/ICCE-Asia57006.2022.9954670
협약과제
22IR1900, 부동산문화재 안전 진단을 위한 지능형 영상정보 분석기반 손상탐지 및 경보 기술 개발, 이상윤
초록
Since supervised learning requires expert labeling work that requires a lot of time and money to acquire data, an alternative is required in the field of cultural property management. In addition, the existing method based on contact type sensors to detect displacement occurring in wooden cultural heritage has a risk of damage to cultural heritage. This paper proposes to apply an artificial intelligence model using f-AnoGAN, which shows good performance in detecting abnormalies, to overcome the difficulty of obtaining abnormal data in wooden cultural assets with an unsupervised learning approach and to explore alternatives to the conventional contact type sensor-based method. The applied anomaly detection model, f-AnoGAN, is characterized by the fact that learning is performed to simultaneously minimize the error in the image space and the error in the feature space when a new image is mapped to the latent space and then returned to the image space. Our experimental results show that the f-AnoGAN model successfully performs judgment on whether or not displacement has occurred in wooden cultural properties using actual CCTV color images, and further presents the location of displacement when displacement occurs.
KSP 제안 키워드
Color images, Contact type sensor, Cultural Heritage, Cultural assets, Detection model, Feature space, Latent space, Learning approach, Property management, abnormal data, anomaly detection