Date of Publication :28th April 2023

Abstract: The application of machine learning, deep learning, object detection and other related technologies has gradually matured, but in the field of cultural asset protection and art restoration, related applications are just about to begin. In the process of reviewing the restoration status of the works, in the past, it was necessary for the restorer to manually mark the missing position, which resulted in a lot of time and energy consumption, and sometimes there was a possibility of misjudgment due to the restorer's experience. To solve those problems, this study intends to use object detection technology to assist restorers to speed up the efficiency of missing annotations in the work inspection process. This study proposes to use YOLOv5 object detection technology to detect defects in the painted layer of oil paintings, assist restorers to save time in manually marking damaged areas, and devote more time and energy to painting restoration operations. This study uses two non-destructive examination techniques, Ultraviolet and Normal light, to identify retouching, insect excrement, and painted layers loss. hoped this will give some technical assistance to the field of art restoration. Some repair techniques are combined with today's computer technology to improve the effect of repair and reduce the consumption of manpower and time.

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