Semi-automatic crack width measurement using an OrthoBoundary algorithm

被引:15
作者
Li, Zhe [1 ,2 ]
Miao, Yi [3 ]
Torbaghan, Mehran Eskandari [2 ]
Zhang, Hongfei [1 ]
Zhang, Jiupeng [1 ]
机构
[1] Changan Univ, Key Lab Special Area Highway Engn, Minist Educ, Xian 710064, Peoples R China
[2] Univ Birmingham, Sch Engn, Birmingham B15 2TT, England
[3] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, England
关键词
Pavement crack; Width measurement; Orthogonal projection; Principal component analysis; Skeleton; MONTE-CARLO-SIMULATION; IMAGE; RECONSTRUCTION;
D O I
10.1016/j.autcon.2023.105251
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Evaluation of pavements' crack severity levels currently relies heavily on width measurement, which necessitates the development of a rapid, and high-accurate, automatic measurement approach for complex pavement cracks. This paper presents an OrthoBoundary algorithm that leverages the crack boundary and skeleton directions to determine crack propagation. Comparative analysis has been conducted between OrthoBoundary and AreaLength, Skeleton Shortest Distance (SSD), Edge Shortest Distance (ESD), and Orthogonal Projection (OP) methods. Results indicate that the OrthoBoundary algorithm achieves an average accuracy of 90.10%, outperforming the Area-Length (86.60%), SSD (76.01%), ESD (87.24%), and OP (88.07%) methods. Notably, the OrthoBoundary algorithm also exhibits processing speeds approximately 120 times faster than other considered methods while demonstrating improved robustness and user-friendliness. It has significant potential to quantify and assess the severity of pavement cracks, as well as to facilitate maintenance decision-making processes in road infrastructure management systems.
引用
收藏
页数:17
相关论文
共 43 条
[11]   Image-based concrete crack assessment using mask and region-based convolutional neural network [J].
Kim, Byunghyun ;
Cho, Soojin .
STRUCTURAL CONTROL & HEALTH MONITORING, 2019, 26 (08)
[12]   Crack detection limits in unit based masonry with terrestrial laser scanning [J].
Laefer, Debra F. ;
Linh Truong-Hong ;
Carr, Hamish ;
Singh, Manmeet .
NDT & E INTERNATIONAL, 2014, 62 :66-76
[13]   Estimation of crack width based on shape-sensitive kernels and semantic segmentation [J].
Lee, Jun S. ;
Hwang, Sung Ho ;
Choi, Il Yoon ;
Choi, Yeongtae .
STRUCTURAL CONTROL & HEALTH MONITORING, 2020, 27 (04)
[14]   Automatic quantification of crack patterns by image processing [J].
Liu, Chun ;
Tang, Chao-Sheng ;
Shi, Bin ;
Suo, Wen-Bin .
COMPUTERS & GEOSCIENCES, 2013, 57 (57) :77-80
[15]   Deep learning and infrared thermography for asphalt pavement crack severity classification [J].
Liu, Fangyu ;
Liu, Jian ;
Wang, Linbing .
AUTOMATION IN CONSTRUCTION, 2022, 140
[16]   Automated pavement crack detection and segmentation based on two-step convolutional neural network [J].
Liu, Jingwei ;
Yang, Xu ;
Lau, Stephen ;
Wang, Xin ;
Luo, Sang ;
Lee, Vincent Cheng-Siong ;
Ding, Ling .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2020, 35 (11) :1291-1305
[17]   Improved impact assessment of odorous compounds from landfills using Monte Carlo simulation [J].
Liu, Yanjun ;
Lu, Wenjing ;
Wang, Hongtao ;
Gao, Xingbao ;
Huang, Qifei .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 648 :805-810
[18]  
Moussa G., 2011, INT MULT ENG TECHN I, P11, DOI [10.13140/2.1.3191.2001, DOI 10.13140/2.1.3191.2001]
[19]   Risk Assessment in Bridge Construction Projects in Iran Using Monte Carlo Simulation Technique [J].
Naderpour, Hosein ;
Kheyroddin, Ali ;
Mortazavi, Seyedmehdi .
PRACTICE PERIODICAL ON STRUCTURAL DESIGN AND CONSTRUCTION, 2019, 24 (04)
[20]   Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning [J].
Ni, FuTao ;
Zhang, Jian ;
Chen, ZhiQiang .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2019, 34 (05) :367-384