A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory

One of the main distresses that occur in the road surface is a result of pavement cracking. This article proposes a new pavement crack detection method that combines two-dimensional (2D) gray-scale images and three-dimensional (3D) laser scanning data based on Dempster-Shafer (D-S) theory. The 2D gray-scale image and 3D laser scanning data are modeled as a mass function in evidence theory in this model and the 2D and 3D detection results for pavement cracks are fused at the decision-making level. The proposed method takes advantage of the respective merits of 2D images and 3D laser scanning data and therefore improves the pavement crack detection accuracy and reduces recognition error rate compared to 2D image intensity-based methods. This article discusses how objective and accurate detection or evaluation for these cracks is an important task in the pavement maintenance and management for state highway departments of transportation.

[1]  John Trinder,et al.  Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection , 2005, Inf. Fusion.

[2]  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 .

[3]  Serge P. Hoogendoorn,et al.  A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways , 2010, Comput. Aided Civ. Infrastructure Eng..

[4]  Byoung Jik Lee,et al.  Position‐Invariant Neural Network for Digital Pavement Crack Analysis , 2004 .

[5]  Ahmed Bouridane,et al.  2D and 3D palmprint information, PCA and HMM for an improved person recognition performance , 2013, Integr. Comput. Aided Eng..

[6]  Chunsun Zhang,et al.  An Unmanned Aerial Vehicle‐Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses 1 , 2012, Comput. Aided Civ. Infrastructure Eng..

[7]  Kari Sentz,et al.  Combination of Evidence in Dempster-Shafer Theory , 2002 .

[8]  Hojjat Adeli,et al.  System identification in structural engineering , 2012 .

[9]  Laurent Jacques,et al.  A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity , 2011, Signal Process..

[10]  Guangjun Zhang,et al.  Complete calibration of a structured light stripe vision sensor through planar target of unknown orientations , 2005, Image Vis. Comput..

[11]  Ming Liang,et al.  Wavelet‐Based Detection of Beam Cracks Using Modal Shape and Frequency Measurements , 2012, Comput. Aided Civ. Infrastructure Eng..

[12]  Naoki Tanaka,et al.  A Crack Detection Method in Road Surface Images Using Morphology , 1998, MVA.

[13]  Yue Min Zhu,et al.  The combined use of the evidence theory and fuzzy logic for improving multimodal nondestructive testing systems , 2005, IEEE Transactions on Instrumentation and Measurement.

[14]  Chun C. Lai,et al.  Multisensor Fusion and Integration: Theories, Applications, and its Perspectives , 2011, IEEE Sensors Journal.

[15]  Martin Treiber,et al.  Reconstructing the Traffic State by Fusion of Heterogeneous Data , 2009, Comput. Aided Civ. Infrastructure Eng..

[16]  Kelvin C. P. Wang.  Designs and Implementations of Automated Systems for Pavement Surface Distress Survey , 2000 .

[17]  Kelvin C. P. Wang,et al.  Wavelet-Based Pavement Distress Image Edge Detection with À Trous Algorithm , 2007 .

[18]  E. Salari,et al.  Automated pavement distress inspection based on 2D and 3D information , 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY.

[19]  F. Blais,et al.  Automated pavement distress data collection and analysis: a 3-D approach , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[20]  Kelvin C P Wang,et al.  Automated Pavement Distress Survey: A Review and A New Direction , 2002 .

[21]  Marc Moonen,et al.  Joint DOA and multi-pitch estimation based on subspace techniques , 2012, EURASIP J. Adv. Signal Process..

[22]  Francisco Angel Moreno,et al.  An Instrumented Vehicle for Efficient and Accurate 3D Mapping of Roads , 2013, Comput. Aided Civ. Infrastructure Eng..

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

[24]  Xin Jiang,et al.  Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform , 2012, Comput. Aided Civ. Infrastructure Eng..

[25]  Alessandra Bianchini,et al.  Fuzzy Representation of Pavement Condition for Efficient Pavement Management , 2012, Comput. Aided Civ. Infrastructure Eng..

[26]  Xiaoming Sun,et al.  Decision model in the laser scanning system for pavement crack detection , 2011 .

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

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

[29]  J Laurent,et al.  Development of a new 3D transverse laser profiling system for the automatic measurement of road cracks , 2008 .

[30]  Max Monti,et al.  Large-area laser scanner with holographic detector optics for real-time recognition of cracks in road surfaces , 1995 .

[31]  Giovanna Sansoni,et al.  State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation , 2009, Sensors.

[32]  Jing Wang,et al.  A wavelet-based particle swarm optimization algorithm for digital image watermarking , 2012, Integr. Comput. Aided Eng..

[33]  Andreas Koschan,et al.  3D reconstruction of road surfaces using an integrated multi-sensory approach , 2007 .

[34]  Ezzatollah Salari,et al.  Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification , 2010, Comput. Aided Civ. Infrastructure Eng..

[35]  Xin Chen,et al.  Road Detection on Shape Features and D–S Evidence in Remote Sensing Images , 2012 .

[36]  Makoto Nagao,et al.  Automatic Pavement-Distress-Survey System , 1990 .

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

[38]  Yozo Fujino,et al.  Concrete Crack Detection by Multiple Sequential Image Filtering , 2012, Comput. Aided Civ. Infrastructure Eng..

[39]  Bugao Xu,et al.  Automatic inspection of pavement cracking distress , 2006, J. Electronic Imaging.

[40]  Hojjat Adeli,et al.  A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning , 2007, Comput. Aided Civ. Infrastructure Eng..

[41]  Heng-Da Cheng,et al.  Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory , 1999 .

[42]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings , 2005 .

[43]  Kelvin C. P. Wang,et al.  Experimentation of 3D Pavement Imaging through Stereovision , 2007 .

[44]  Panos Liatsis,et al.  Improving fusion with optimal weight selection in Face Recognition , 2012, Integr. Comput. Aided Eng..

[45]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

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

[47]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[48]  Hui Gao,et al.  A Markov‐Based Road Maintenance Optimization Model Considering User Costs , 2013, Comput. Aided Civ. Infrastructure Eng..

[49]  H. Elbehiery,et al.  Surface Defects Detection for Ceramic Tiles Using Image Processing and Morphological Techniques , 2007, WEC.

[50]  Nasir G. Gharaibeh,et al.  A Spatial‐Bayesian Technique for Imputing Pavement Network Repair Data , 2012, Comput. Aided Civ. Infrastructure Eng..