基于计算机视觉的藏式古建筑石砌体壁画墙裂缝生长变形监测  

DEFORMATION MONITORING DURING THE GROWTH OF CRACKS IN MURAL WALLS WITH STONE MASONRY MATERIAL OF TIBETAN ANCIENT BUILDINGS BASED ON COMPUTER VISION

作　　者：杨娜[1] 王烁 汪德佳 YANG Na;WANG Shuo;WANG De-jia(School of Civil Engineering,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学土木建筑工程学院,北京100044

出　　处：《工程力学》2025年第1期129-142,共14页Engineering Mechanics

基　　金：中央高校基本科研业务费专项资金项目(2021JBZ110);国家自然科学基金面上项目“基于计算机视觉的多尺度古建筑结构变形监测技术与评估方法研究”(52478119);国家自然科学基金面上项目“累积残损变形对古建筑木结构力学性能的影响”(51878034)。

摘　　要：对单一背景下裂缝的定期检测研究已取得一定成果,但对复杂背景下裂缝预防性长期生长变形监测的研究尚处于起步阶段。该文综合古建筑壁画墙变形微量和不宜扰动的特点,基于计算机视觉研究了传统图像分割处理技术和裂缝图像智能语义分割神经网络模型,建立了一套非接触式、预防性生长变形监测系统。为降低壁画墙裂缝特有的彩绘壁画、环境光照及噪声等干扰,有效地将裂缝从复杂背景中分离出来,在传统阈值分割算法系统中,通过SIFT特征匹配和单应性矩阵的求解,解决图像视角差异问题,通过对比不同滤波算法,选择更适用于壁画墙裂缝分割的双边滤波算法与阈值分割相结合的监测算法;在智能语义分割系统中,采用多层卷积、采样和拼接等操作,去除多余特征,重构裂缝高级语义特征图,选择多步优化策略改进原U-Net模型网络架构,提升模型测试平均准确率至0.9899。监测12天典型藏式古建筑石砌体壁画墙裂缝,提取裂缝轮廓及裂缝骨架线等相关特征参数作为关键指标定量描述裂缝生长变化信息,发现:传统阈值分割算法二维特征指标(如裂缝面积、密度、宽度)的变异系数COV值处于4.50%~6.52%,改进的U-Net模型将传统方案中数据波动最大的裂缝面积COV由6.52%降至3.53%,提高了监测系统对壁画色彩、光照和阴影干扰的鲁棒性;系统中两类算法分别处理了不同视角下的同一裂缝的12张图像,输出的数据具备均匀一致性,COV不超过7%,证明了该监测系统为壁画墙裂缝的生长变形提供实时无损监测的技术可行性。Numerous achievements have been made in the research of conventional fracture detection under a single background,but the study on preventive deformation monitoring of cracks under complex backgrounds in long-term conditions is still at its early stages.Considering that the mural walls of ancient buildings have the characteristics of micro-deformation and non-interference,a non-contact and preventive deformation monitoring system has been established.This system is based on computer vision technology,including traditional image segmentation processing technology,as well as a neural network model for semantic segmentation of crack images.In order to reduce the interference of color-painted murals,environmental lighting,and noise that are unique to cracks in mural walls and effectively separate the cracks from the complex background,traditional threshold segmentation algorithm systems and intelligent semantic segmentation systems are studied.The former system utilizes feature matching of scale-invariant feature transform and the solution of homography matrix to solve the problem of image perspective differences.Meanwhile,by comparing different filtering algorithms,a monitoring algorithm is selected,which combines bilateral filtering and threshold segmentation that is more suitable for mural crack segmentation.The latter system uses multi-layer convolution,sampling and splicing operations to remove redundant features,reconstruct the high-level semantic feature map of cracks,and select multi-step optimization strategy to improve the original U-Net model network architecture,which improves the average accuracy of model to 0.9899.The crack of typical mural walls with stone masonry material of Tibetan ancient buildings has been monitored for 12 days,and relevant characteristic parameters such as crack contours and crack skeleton lines are extracted as key indicators to quantitatively describe the changing information during the crack growth process.The results show that the coefficient of variation(COV)of the two-dimens

关 键 词：藏式古建筑壁画墙 裂缝 阈值分割算法 神经网络模型 特征参数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]