Recognition of Damage Types on the Great Wall Surface Based on Machine Learning: Taking the Shanhaiguan Great Wall as an Example(Training set for machine learning)

The Shanhaiguan Great Wall is a part of the Ming dynasty Great Wall, which is a world heritage site. Its basic structure is filled with rammed earth and gray bricks on both sides. Due to environmental influences, gray bricks on the surface will catch some damages, resulting in a decline in the quality of their structure and even threatening their safety. Traditional surface damage detection is mainly based on manual identification or manual identification after UAV aerial photography, and this will be costly in human resources. This paper uses the YOLOv4 machine learning model, taking the surface gray brick of the plain Great Wall of Shanhaiguan as an example. By slicing and labeling the photos, creating a training set, and then training the model, it automatically finds four types of damage (chalking, plant, ubiquinol, and cracking) on the surface of the Great Wall, which will solve the problem of costly human resources for manual identification after aerial photography, allowing the work to progress faster.山海关长城是世界文化遗产明代长城的组成部分。其主体结构以夯土为芯，两侧砌筑灰砖。受自然环境影响，表层灰砖会出现各类破损，导致结构品质下降，甚至威胁本体安全。传统的表层破损检测主要采用人工直接识别，或先通过无人机（UAV）航拍后开展人工甄别，此类方式人力资源成本高昂。本文以山海关平原段长城表层灰砖为研究对象，采用YOLOv4机器学习模型开展研究：通过对航拍照片进行切片与标注、构建训练集并完成模型训练，该模型可自动识别长城表层四类破损（粉化（chalking）、植物附着（plant）、泛醇（ubiquinol）、开裂（cracking）），可解决航拍后人工甄别所面临的高人力资源成本难题，大幅提升作业效率。