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In this paper, CCTV images of Heunginjimun were collected, and a dataset was constructed to determine the displacement of the roof. We applied artificial displacements to create a virtual database. In addition, to secure the damaged images' validity, some experts reviewed them in the process.
We tested the model's performance by training the model using the built dataset. Among the models tested, the EfficientNetB0 and EfficientNetB2 models have shown high prediction accuracy of 99.61% and 99.55%, respectively, for detecting the roof tilts of Heunginjimun Gate.
This experiment presented a method for securing datasets to overcome difficulties in obtaining damage data due to the nature of cultural properties and a method for quickly detecting abnormal signs for wooden cultural properties.
The framework's robustness can be further improved by adding more diverse displacement scenarios in future experiments and applying a dataset that mixes environments for learning.
The suppose models are designed and tested for roof inclination and other types of displacement such as fullness and separation. Therefore it is expected to help significantly manage and preserve cultural properties.
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IEEE/ICACT20230153 Slide.13
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