Recognition of Damage Types of gray-brick Ancient Buildings Based on Machine Learning——Taking the Macau World Heritage Buffer Zone as an Example(Training set for machine learning)
Description
As a result of environmental and human influences, several types of surface deterioration emerge on historic buildings, resulting in a decline in the quality of these structures and even threats to their safety. In the conventional approach, assessing the surface damage on a structure involves the time-consuming and labor-intensive judgment and evaluation of trained professionals. In this study, it is suggested that the YOLOv4 machine learning model be used to automatically find five types of damage to historical gray-brick buildings. This would make the job go more quickly. This study uses the gray-brick wall buildings in the buffer zone of the global cultural heritage in Macau as an example. In total, 1355 photographs were taken on-site of the gray-brick walls, and the five most common types of damage were identified. By slicing and labeling the photos, a training set of 1000 images was created, and through 200-generation model training, the model can accurately identify and effectively identify the damage state of the gray bricks and enhance the quality judgment and evaluation of the exterior walls of historical buildings. Experiments allow us to reach the following conclusions: (1) The damage to the gray-brick ancient buildings in Macau is affected by the subtropical maritime climate. Missing paint, stains, and cracks are the main contributors to gray-brick wall damage. (2) Machine learning can help determine the type of damage to old gray-brick buildings, which is useful for managing and protecting historical buildings. (3) The model in this study can identify five types of damage: missing, cracking, plant or microbial erosion, yellowing, and pollution on the exterior walls of ancient gray-brick buildings. It is helpful to accurately identify and evaluate the damaged condition of the gray-brick wall and formulate corresponding protection schemes.
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Funding
National Social Science Fund of China
21VJXT011