基于计算机视觉的古城墙智能缺陷检测
Gengpei Zhang1, Xiaohan Dou1, Leqi Li1
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了计算机视觉框架,用于检测古城墙上的缺陷. 该系统提高了图像质量,并使用人工智能识别裂和侵蚀,帮助保护文化遗产.
科学领域:
- 遗产保护科学
- 计算机视觉
- 数字遗产
背景情况:
- 古城墙是文化遗产的重要载体,易受环境因素和人类活动造成的裂和侵蚀.
- 有效的保护需要先进的监测技术来识别和解决这些缺陷.
- 现有的方法可能缺乏复杂遗产所需的精度和全面分析.
研究的目的:
- 使用计算机视觉开发和验证古城墙的多阶段缺陷检测框架.
- 提高识别各种缺陷的准确性和效率,例如裂,缺失,盐结晶和植被侵蚀.
- 为智能监测和保护历史建筑提供技术基础.
主要方法:
- 一个涉及图像处理 (Restormer,SG-LLIE),缺陷样本增大 (LFS-GAN),2D缺陷检测 (YOLOv12),深度估计和3D重建 (高斯分片) 的管道.
- 使用先进的AI模型进行图像增强,缺陷识别和空间分析.
- 集成的深度估计用于缺陷验证和高斯喷涂用于3D可视化.
主要成果:
- 该框架成功检测了京州古城墙的多种缺陷.
- 图像增强模型显著提高了分析墙壁图像质量.
- 通过增强数据训练的YOLOv12网络显示出有效的二维缺陷识别.
- 3D重建提供了检测到的缺陷的详细视觉表示.
结论:
- 拟议的计算机视觉框架为古城墙的智能监控提供了强大的解决方案.
- 这项技术为加强文化遗产保护工作提供了重要的理论和技术支持.
- 多个阶段的方法有效地解决了复杂的历史结构中缺陷检测的挑战.
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