A Deep-Learning Method Using Auto-encoder and Generative Adversarial
  Network for Anomaly Detection on Ancient Stone Stele Surfaces
Yikun Liu Yuning Wang Cheng Liu
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.