Automatic image acquisition with knowledge-based approach for multi-directional determination of skid resistance of pavements

Abstract Evaluation of pavement skid resistance (SR), as a crucial index for assessing the degree of pavement safety, is essential for pavement management. Considering the difficulties involved in estimation of an overall index of extent and direction of SR, a new method is proposed in the present work for estimation of the SR using an automated image-based system. The images were first captured by an automated image acquisition system (IAS) and then an image processing expert system and a knowledge-based decision support system (DSS) were designed. The central part of the system proposed in the current work runs based on Wavelet Transform (WT), which consists of distinct modules including pre-processing, feature extraction, approximate indexes in three different directions (horizontal, vertical, and diagonal), and the overall index. The method was verified on a database of pavement images collected in dry and wet conditions. A comparison of the obtained results with those of British pendulum tester (BPT) indicates the validity and high speed of the proposed method.

[1]  Ching-Yao Chan,et al.  Evaluation of Traffic and Environment Effects on Skid Resistance in California , 2010 .

[2]  Philippe Bolon,et al.  2-D Wavelet Packet Spectrum for Texture Analysis , 2013, IEEE Transactions on Image Processing.

[3]  Fajaruddin Mustakim.  Treating hazardous locations at federal route 50 , 2005 .

[4]  S Iyinam,et al.  Prediction of Road Surface Friction Coefficient Using Only Macro- and Microtexture Measurements , 2005 .

[5]  Gerardo W. Flintsch,et al.  Pavement Surface Macrotexture Measurement and Applications , 2003 .

[6]  Stefano Manzoni,et al.  Laser-triangulation device for in-line measurement of road texture at medium and high speed , 2010 .

[7]  A Dunford,et al.  Measuring skid resistance without contact , 2008 .

[8]  E. D. de León Izeppi,et al.  Pavement macro-texture analysis using wavelets , 2013 .

[9]  K L Smith,et al.  Guide for Pavement Friction , 2009 .

[10]  J C Wambold,et al.  INTERNATIONAL PIARC EXPERIMENT TO COMPARE AND HARMONIZE TEXTURE AND SKID RESISTANCE MEASUREMENT. , 1994 .

[11]  Amiruddin Ismail,et al.  An Overview of Expert Systems in Pavement Management , 2009 .

[12]  Ibrahim M. Asi,et al.  Evaluating skid resistance of different asphalt concrete mixes , 2007 .

[13]  José M Pardillo Mayora,et al.  An assessment of the skid resistance effect on traffic safety under wet-pavement conditions. , 2009, Accident; analysis and prevention.

[14]  Manjriker Gunaratne,et al.  Evaluation of the Effect of Pavement Roughness on Skid Resistance , 2010 .

[15]  Eyad Masad,et al.  Relationship of Aggregate Texture to Asphalt Pavement Skid Resistance Using Image Analysis of Aggregate Shape , 2007 .

[16]  Arash Rezaei,et al.  Field Evaluation of Asphalt Mixture Skid Resistance and its Relationship to Aggregate Characteristics , 2010 .

[17]  Fereidoon Moghadas Nejad,et al.  An optimum feature extraction method based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification , 2011, Expert Syst. Appl..

[18]  Ambarish Banerjee,et al.  Influence of pavement condition on horizontal curve safety. , 2013, Accident; analysis and prevention.

[19]  Fereidoon Moghadas Nejad,et al.  The Hybrid Method and its Application to Smart Pavement Management , 2013 .

[20]  Mohamed Abdel-Aty,et al.  Design and verification of a laser based device for pavement macrotexture measurement , 2011 .

[21]  Fereidoon Moghadas Nejad,et al.  An expert system based on wavelet transform and radon neural network for pavement distress classification , 2011, Expert Syst. Appl..

[22]  Brian D Prowell,et al.  Evaluation of Circular Texture Meter for Measuring Surface Texture of Pavements , 2005 .

[23]  Shu-Hsien Liao,et al.  Expert system methodologies and applications - a decade review from 1995 to 2004 , 2005, Expert Syst. Appl..

[24]  Sou-Sen Leu,et al.  Digital image processing based approach for tunnel excavation faces , 2005 .

[25]  R Elunai,et al.  Digital image processing techniques for pavement macro-texture analysis , 2010 .

[26]  Shamik Tiwari,et al.  Performance Analysis of Texture Image Classification Using Wavelet Feature , 2013 .

[27]  Arash Rezaei,et al.  Predicting Asphalt Mixture Skid Resistance by Aggregate Characteristics and Gradation , 2009 .

[28]  Douglas D. Gransberg,et al.  Road surface texture measurement using digital image processing and information theory , 2006 .

[29]  Sigit Pranowo Hadiwardoyo,et al.  The influence of Buton asphalt additive on skid resistance based on penetration index and temperature , 2013 .

[30]  Cengiz Kahraman,et al.  An expert system towards solving ship auxiliary machinery troubleshooting: SHIPAMTSOLVER , 2009, Expert Syst. Appl..

[31]  Gerardo W Flintsch,et al.  Area-Based Macrotexture Measurements: Stereo Vision Approach , 2009 .

[32]  Vinod Chandran,et al.  Asphalt Concrete Surfaces Macrotexture Determination From Still Images , 2011, IEEE Transactions on Intelligent Transportation Systems.