A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces
This addresses the need for accurate, efficient, and cost-effective anomaly detection in cultural heritage preservation, though it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackles the problem of detecting natural and man-made damage on ancient stone stele surfaces for preventive conservation, achieving a reconstruction accuracy of 99.74% and proficient detection of seven anomalies without false alarms.
Accurate detection of natural deterioration and man-made damage on the surfaces of ancient stele in the first instance is essential for their preventive conservation. Existing methods for cultural heritage preservation are not able to achieve this goal perfectly due to the difficulty of balancing accuracy, efficiency, timeliness, and cost. This paper presents a deep-learning method to automatically detect above mentioned emergencies on ancient stone stele in real time, employing autoencoder (AE) and generative adversarial network (GAN). The proposed method overcomes the limitations of existing methods by requiring no extensive anomaly samples while enabling comprehensive detection of unpredictable anomalies. the method includes stages of monitoring, data acquisition, pre-processing, model structuring, and post-processing. Taking the Longmen Grottoes' stone steles as a case study, an unsupervised learning model based on AE and GAN architectures is proposed and validated with a reconstruction accuracy of 99.74\%. The method's evaluation revealed the proficient detection of seven artificially designed anomalies and demonstrated precision and reliability without false alarms. This research provides novel ideas and possibilities for the application of deep learning in the field of cultural heritage.