Detection of cracks on concrete surfaces by hyperspectral image processing

All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly discretized for crack detection on concrete surfaces, considering cracking combined with the most usual concrete anomalies, namely biological colonization.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Eduardo Júlio,et al.  Automatic concrete health monitoring: assessment and monitoring of concrete surfaces , 2014 .

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

[4]  C. Maierhofer,et al.  MULTI-SPECTRAL DATA ACQUISITION AND PROCESSING TECHNIQUES FOR DAMAGE DETECTION ON BUILDING SURFACES , 2006 .

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

[6]  P. Pattynama,et al.  Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. , 1998, European journal of radiology.

[7]  S. Hashimoto,et al.  Automated Crack Detection for Concrete Surface Image Using Percolation Model and Edge Information , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

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

[9]  Bora Dibya Jyoti.  A Novel Color Image Segmentation Approach Based On K-Means Clustering with Proper Determination of the Number of Clusters and Suitable Distance Metric , 2016 .

[10]  Shuji Hashimoto,et al.  Image‐Based Crack Detection for Real Concrete Surfaces , 2008 .

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  Shuji Hashimoto,et al.  Practical image measurement of crack width for real concrete structure , 2009 .

[13]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[14]  Yoshihiko Hamamoto,et al.  A Method for Crack Detection on a Concrete Structure , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

[17]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[18]  S. Hashimoto,et al.  An efficient crack detection method using percolation-based image processing , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[19]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[20]  Yoshihiko Hamamoto,et al.  A robust automatic crack detection method from noisy concrete surfaces , 2011, Machine Vision and Applications.

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

[22]  Tara C. Hutchinson,et al.  Improved image analysis for evaluating concrete damage , 2006 .

[23]  Eduardo Júlio,et al.  Damage assessment on concrete surfaces using multi-spectral image analysis , 2013 .

[24]  J. L. Lerma,et al.  Multitemporal thermal analysis to detect moisture on a building façade , 2011 .

[25]  Shuji Hashimoto,et al.  Improved percolation-based method for crack detection in concrete surface images , 2008, 2008 19th International Conference on Pattern Recognition.