Robust Automated Concrete Damage Detection Algorithms for Field Applications

This paper presents a computer vision framework supporting automated infrastructure damage detection, with a specific focus on surface crack detection in concrete. The approach presented is designed to provide a significant increase in robustness relative to existing methods when faced with widely varying field conditions while operating fast enough to be used in large scale applications. In particular, a clustering method for segmentation is developed that exploits inherent characteristics of fracture images to achieve consistent performance, combined with robust feature extraction to improve recognition algorithm classifier outcomes. The approach is shown to perform well in detecting cracks across a broad range of surface and lighting conditions, which can cause existing techniques to exhibit significant reductions in detection accuracy and/or detection speed.

[1]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[2]  Christoph Walter,et al.  Design considerations of robotic system for cleaning and inspection of large‐diameter sewers , 2012, J. Field Robotics.

[3]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[4]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

[5]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

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

[7]  Bhabatosh Chanda,et al.  On image enhancement and threshold selection using the graylevel co-occurence matrix , 1985, Pattern Recognit. Lett..

[8]  Sherif Yehia,et al.  PCA-Based algorithm for unsupervised bridge crack detection , 2006, Adv. Eng. Softw..

[9]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[10]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[11]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

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

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

[17]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[18]  James H. Garrett,et al.  Visual Pattern Recognition Supporting Defect Reporting and Condition Assessment of Wastewater Collection Systems , 2009 .

[19]  Chang-Soo Han,et al.  Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[22]  Dulcy M. Abraham,et al.  NEURO-FUZZY APPROACHES FOR SANITARY SEWER PIPELINE CONDITION ASSESSMENT , 2001 .

[23]  P. Dodwell.  Visual Pattern Recognition , 1970 .

[24]  Gaurav S. Sukhatme,et al.  A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures , 2009 .

[25]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[26]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[27]  Tinku Acharya,et al.  Image Processing: Principles and Applications , 2005, J. Electronic Imaging.

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

[29]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[30]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[32]  Bidyut Baran Chaudhuri,et al.  Texture Segmentation Using Fractal Dimension , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Pedro Larrañaga,et al.  An empirical comparison of four initialization methods for the K-Means algorithm , 1999, Pattern Recognit. Lett..

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

[35]  Ioannis Brilakis,et al.  Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation , 2011 .