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학술대회 GAN based Deep Learning Model for Detecting Damage and Displacement of Cultural Asset
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저자
최우연, 박정우, 이상윤
발행일
202110
출처
International Conference on Consumer Electronics (ICCE) 2021 : Asia, pp.82-85
DOI
https://dx.doi.org/10.1109/ICCE-Asia53811.2021.9641996
협약과제
21IR4300, 부동산문화재 안전 진단을 위한 지능형 영상정보 분석기반 손상탐지 및 경보 기술 개발, 이상윤
초록
The following paper proposes F-AnoGan model for anomaly detection regardless of the types of damage or displacement in wooden cultural assets image. Firstly, by training WGAN's Generator model and Discriminator model using Normal images, we can get the value of parameter that is good at creating fake normal images. After the WGAN's parameter is fixed, the Encoder is trained by the normal image that was used to train. Then, we input the new image data of wooden cultural assets to well-trained WGAN and Encoder and calculate Anomaly Score to discriminate and detect area where anomaly exists. The virtual dataset is established with normal data of cultural assets collected and abnormal data that contains anomaly made using Photoshop. The final experimental results confirmed that model cannot detect minute abnormal regions of abnormal images but accurately determines whether it is normal or abnormal. The proposed method from these results show that it is suitable for future anomaly detection of damage and displacement in wooden cultural assets.
키워드
Anomaly Detection, GAN, Unsupervised Learning
KSP 제안 키워드
Cultural assets, Detection of damage, Image data, Learning model, abnormal data, anomaly detection, anomaly score, deep learning(DL), generator model, normal image, scattering parameters(S-parameters)