Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods

Monitoring the instantaneous and changing concrete surface condition is paramount to cost-effectively managing tunnel assets. In practice, detecting cracks efficiently and accurately is a very challenging task due to concrete blebs, stains, and illumination over the concrete surface. Unclear and tiny cracks cannot be detected effectively. In this paper, we proposed an ultra-efficient crack detection algorithm (CrackHHP) and an improved pre-extraction and second percolation process based on the percolation model to address these issues. Our contributions are shown as follows: 1) apply the overlapping grids and weight-based, redefined pixel value to obtain the candidate dark pixel image while preserving the cracks. 2) introduce the second percolation processing to generate a high-accuracy crack detection algorithm, which can connect the tiny fractures and detect the tiny cracks. 3) construct a high-efficiency and high-accuracy crack detection algorithm combining the improved pre-extraction and the second percolation process. The experimental results demonstrate that CrackHHP can significantly improve the efficiency and accuracy of crack detection.

[1]  I. Iervolino,et al.  Computer Aided Civil and Infrastructure Engineering , 2009 .

[2]  Habibollah Danyali,et al.  Salient object detection using local, global and high contrast graphs , 2018, Signal Image Video Process..

[3]  Yichang James Tsai,et al.  Implementation of automatic crack evaluation using Crack Fundamental Element , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  Fan Meng,et al.  Pavement Distress Detection Using Random Decision Forests , 2015, ICDS.

[5]  Paulo Lobato Correia,et al.  Automatic Road Crack Detection and Characterization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[6]  Lu Sun,et al.  Weighted Neighborhood Pixels Segmentation Method for Automated Detection of Cracks on Pavement Surface Images , 2016, J. Comput. Civ. Eng..

[7]  Jérôme Idier,et al.  Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection , 2016, IEEE transactions on intelligent transportation systems (Print).

[8]  Wei Xu,et al.  Pavement crack detection based on saliency and statistical features , 2013, 2013 IEEE International Conference on Image Processing.

[9]  Paul Fieguth,et al.  Segmentation of buried concrete pipe images , 2006 .

[10]  Takashi Matsumoto,et al.  Development of an Automatic Detector of Cracks in Concrete Using Machine Learning , 2017 .

[11]  Yun Yong Kim,et al.  Automated image processing technique for detecting and analysing concrete surface cracks , 2013 .

[12]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[13]  Russell M. Mersereau,et al.  Critical Assessment of Pavement Distress Segmentation Methods , 2010 .

[14]  S. K. Ghorai,et al.  Detection and monitoring of multiple cracks using distributed fiber optic sensor , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[15]  Shuji Hashimoto,et al.  Fast crack detection method for large-size concrete surface images using percolation-based image processing , 2010, Machine Vision and Applications.

[16]  Eduardo Júlio,et al.  Automatic crack monitoring using photogrammetry and image processing , 2013 .

[17]  Manuel Avila,et al.  Free-form anisotropy: A new method for crack detection on pavement surface images , 2011, 2011 18th IEEE International Conference on Image Processing.

[18]  Eduardo Júlio,et al.  Characterisation of concrete cracking during laboratorial tests using image processing , 2012 .

[19]  Nigel Waters,et al.  Review of remote sensing methodologies for pavement management and assessment , 2015 .

[20]  Pedro Arias-Sánchez,et al.  Assessment of cracks on concrete bridges using image processing supported by laser scanning survey , 2017 .

[21]  Qingquan Li,et al.  CrackTree: Automatic crack detection from pavement images , 2012, Pattern Recognit. Lett..

[22]  Pedro Arias,et al.  Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data , 2017 .

[23]  Kristin J. Dana,et al.  Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.

[24]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[25]  Ling Liu,et al.  The algorithm of accelerated cracks detection and extracting skeleton by direction chain code in concrete surface image , 2016 .

[26]  S. Chambon,et al.  Automatic Road Pavement Assessment with Image Processing: Review and Comparison , 2011 .

[27]  Ning Wang,et al.  An improved algorithm for image crack detection based on percolation model , 2015 .

[28]  Luigi Barazzetti,et al.  Crack measurement: Development, testing and applications of an automatic image-based algorithm , 2009 .

[29]  Abdul Ghafoor,et al.  Saliency detection using contrast enhancement and texture smoothing operations , 2017, Signal, Image and Video Processing.