Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques

In this paper, a comprehensive automatic visual inspection system for detecting pavement cracks, built around a Laser Road Inspection System (LRIS) onboard an instrumented vehicle, is presented. Two inertial profilers, a Differential Global Position System (DGPS), a high-definition camera and a high-speed area scan camera are the additional acquisition equipment. Visual appearance and geometrical information are obtained simultaneously since 3D profiles are obtained by capturing the laser line projected by the LRIS with the external area scan camera. Using AdaBoost algorithm for the combination of the processing results of these two types of data allows us to improve surface crack detection rates.

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

[2]  Vu Nguyen,et al.  Automated Survey of Pavement Distress Based on 2D and 3D Laser Images , 2011 .

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

[4]  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.

[5]  José R. Perán,et al.  Automated visual classification of frequent defects in flat steel coils , 2011 .

[6]  Sylvie Chambon,et al.  Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost , 2012, Comput. Aided Civ. Infrastructure Eng..

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

[8]  Gonzalo R. Arce,et al.  Multiresolution Information Mining for Pavement Crack Image Analysis , 2012 .

[9]  Guanqun Bao,et al.  Road Distress Analysis using 2D and 3D Information , 2010 .

[10]  Ezzatollah Salari,et al.  Pavement distress detection and classification using a Genetic Algorithm , 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[11]  Feng Li,et al.  Critical Assessment of Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology , 2012 .

[12]  Sylvie Chambon.  Detection of Road Cracks with Multiple Images , 2010, VISAPP.

[13]  Paulo Lobato Correia,et al.  Identifying and retrieving distress images from road pavement surveys , 2008, 2008 15th IEEE International Conference on Image Processing.

[14]  Vivek Kaul,et al.  Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system , 2012, J. Electronic Imaging.

[15]  Paulo Lobato Correia,et al.  Improved road crack detection based on one-class Parzen density estimation and entropy reduction , 2010, 2010 IEEE International Conference on Image Processing.

[16]  Edward K. Wong,et al.  PAVEMENT DISTRESS ANALYSIS USING IMAGE PROCESSING TECHNIQUES , 1991 .

[17]  Roberto Medina,et al.  Automated Visual Inspection of Road Surface Cracks , 2010 .

[18]  Kelvin C. P. Wang,et al.  Precision Test of Cracking Surveys with the Automated Distress Analyzer , 2011 .

[19]  Nii O. Attoh-Okine,et al.  Hierarchical Markov Chain Monte Carlo Simulation for Modeling Transverse Cracks in Highway Pavements , 2012 .

[20]  Xiaoming Sun,et al.  A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory , 2014, Comput. Aided Civ. Infrastructure Eng..

[21]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[22]  Qingquan Li,et al.  Pavement Crack Classification via Spatial Distribution Features , 2011, EURASIP J. Adv. Signal Process..

[23]  Yichang Tsai,et al.  Critical Assessment of Measuring Concrete Joint Faulting Using 3D Continuous Pavement Profile Data , 2012 .