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Regular inspection and maintenance work is required to secure the structural stability of wooden cultural properties that are deteriorating due to deterioration and artificial activities.
However, there are many restrictions on investigation for safety inspection due to the uniqueness of cultural heritage itself. Therefore, the inspection process is usually conducted by methods such as visual inspection and non-destructive ones.
However, this type of regular inspection has limitations in terms of time and cost. Recently, various studies have been conducted to predict damage or deformation of cultural properties in advance by using Deep Learning technology and coping with the possible problems in the early stages.
In recent studies [3], [4], [5], a Deep Learning model that measures the amount of displacement using a generative model was presented.
In this paper, we propose a method for normal-abnormal binary classification using CCTV image data of the roof of Heunginjimun, Korea's treasure No. 1. To this end, six image datasets were created, and by using them, we propose a Deep Learning framework that can detect subtle tilts that are difficult to be identified even with the human eye.
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IEEE/ICACT20230153 Slide.02
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