摘要
裂缝是隧道衬砌最常见也是最严重的病害之一。针对常规图像识别方法存在的问题,提出一种基于图像局部网格特征的隧道衬砌裂缝自动识别方法。首先将图像划分为8 Pixel×8 Pixel的局部网格,基于局部网格内不同方向之间的亮度差异和对比度差异特征设计十字形模板,通过模板计算将网格中灰度值最小的像素识别为潜在的裂缝种子,最后采用种子连接算法将离散的裂缝种子像素连接成为完整的连续裂缝,在连接过程中自动计算裂缝的走向、长度和宽度。通过接受者操作特征曲线估计算法的最优参数和最佳阈值,从定性和定量分析两方面验证其可靠性和准确性。工程应用实例表明,算法能取得良好的裂缝识别效果,特别是对细微裂缝和存在渗漏水的衬砌图像,算法的可靠性和识别率均高于常规的图像识别方法。
Crack is one of the most common and serious defects in tunnel lining.In light of the existing problems of conventional image recognition methods,an automatic crack recognition method in tunnel lining based on characteristics of local grids in images is presented.A lining image is firstly divided into local grids of 8 Pixel×8 Pixel.Cross-shaped templates are designed based on the characteristics of luminance difference and contrast difference between different directions in local grids.The pixel with minimum gray value in each grid can be recognized as one potential crack seed by template calculation.Discrete crack seeds are finally linked together to form an intact and continuous crack cluster using seed linking algorithm.During the linking process,the direction,length and width of cracks are measured automatically.The optimal parameters and threshold of the proposed algorithm are estimated using receiver operating characteristics（ROC） curves.The reliability and accuracy are validated by means of qualitative and quantitative analyses.Application cases show that the proposed method can achieve good effects of crack recognition,especially for the lining images containing minor cracks and leakage;and the reliability and recognition rate are higher than those of other conventional image recognition methods.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2012年第5期991-999,共9页
Chinese Journal of Rock Mechanics and Engineering
基金
云南省省院省校科技合作计划项目(2008AD013)
长江学者和创新团队发展计划资助项目(IRT1029)
关键词
隧道工程
隧道检测
裂缝识别
局部网格
接受者操作特征曲线
tunnelling engineering
tunnel inspection
crack recognition
local grid
receiver operating characteristics（ROC） curve
作者简介
王平让（1977-），男，2004年于大连理工大学水利工程学院港口、海岸及近海工程专业获硕士学位，现为博士研究生，主要从事隧道及地下工程自动检测和结构安全评估方面的研究工作。E-mail：wpr2002@yahoo．cn
