Crack Detection Using Fast Spectral Clustering Considering Graph Connectivity

Cracks on pavement roads cause various traffic problems. Therefore we should repair them properly. Nowadays a variety of crack detection methods in computer vision have been proposed. Spectral clustering is one of them and an effective method, but suffers from processing time due to the large amount of calculation. Among them, calculating of Laplacian-matrix and eigenvalues especially affect processing time. Therefore we propose two methods to improve the efficiency of algorithm. One applies sparse process considering graph connectivity for Laplacian-matrix, and the other considers amount of pixel of crack of pavement road images.

[1]  Hongtao Lu,et al.  Non-negative and sparse spectral clustering , 2014, Pattern Recognit..

[2]  Giovanni Pascale,et al.  Crack assessment in marble sculptures using ultrasonic measurements: Laboratory tests and application on the statue of David by Michelangelo , 2015 .

[3]  Hanyu Hong,et al.  A new method for automatic detection of rut feature based on road laser images , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[4]  Khurram Kamal,et al.  Pavement crack detection using the Gabor filter , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[5]  Zhang Yiyang,et al.  The design of glass crack detection system based on image preprocessing technology , 2014, 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference.

[6]  Matsushima Kousuke,et al.  Crack Detection using Spectral Clustering based on Crack Features , 2017 .

[7]  Diego González-Aguilera,et al.  Prediction of depth model for cracks in steel using infrared thermography , 2015 .

[8]  Christian Koch,et al.  Pothole detection in asphalt pavement images , 2011, Adv. Eng. Informatics.

[9]  Bachir Boudraa,et al.  Mathematical morphology for TOFD image analysis and automatic crack detection. , 2014, Ultrasonics.

[10]  Diego González-Aguilera,et al.  Thermographic test for the geometric characterization of cracks in welding using IR image rectification , 2016 .

[11]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[12]  Bo Shaobo,et al.  Pavement Crack Detection and Analysis for High-grade Highway , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

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

[14]  Xiaoming Sun,et al.  Pavement crack characteristic detection based on sparse representation , 2012, EURASIP J. Adv. Signal Process..

[15]  A. Zang,et al.  Detection of crack propagation in concrete with embedded ultrasonic sensors , 2015 .

[16]  Devdas Shetty,et al.  Application of a He3Ne infrared laser source for detection of geometrical dimensions of cracks and scratches on finished surfaces of metals , 2013 .

[17]  Peggy Subirats,et al.  Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform , 2006, 2006 International Conference on Image Processing.

[18]  Jian Zhou,et al.  Wavelet-based pavement distress detection and evaluation , 2003 .

[19]  Tanaka Yuichi,et al.  Graph Learning for Spectral Clustering using Low-rank and Sparse Decomposition , 2017 .

[20]  Robyn A. Owens,et al.  2D feature detection via local energy , 1997, Image Vis. Comput..

[21]  Bachir Boudraa,et al.  Automatic Crack Detection and Characterization During Ultrasonic Inspection , 2010 .

[22]  Ignacio Parra,et al.  Adaptive Road Crack Detection System by Pavement Classification , 2011, Sensors.