Asphalt Concrete Surfaces Macrotexture Determination From Still Images

Road surface macrotexture is identified as one of the factors contributing to the surface's skid resistance. Existing methods of quantifying the surface macrotexture, such as the sand patch test and the laser profilometer test, are either expensive or intrusive, requiring traffic control. High-resolution cameras have made it possible to acquire good quality images from roads for the automated analysis of texture depth. In this paper, a granulometric method based on image processing is proposed to estimate road surface texture coarseness distribution from their edge profiles. More than 1300 images were acquired from two different sites, extending to a total of 2.96 km. The images were acquired using camera orientations of 60° and 90°. The road surface is modeled as a texture of particles, and the size distribution of these particles is obtained from chord lengths across edge boundaries. The mean size from each distribution is compared with the sensor measured texture depth obtained using a laser profilometer. By tuning the edge detector parameters, a coefficient of determination of up to R2 = 0.94 between the proposed method and the laser profilometer method was obtained. The high correlation is also confirmed by robust calibration parameters that enable the method to be used for unseen data after the method has been calibrated over road surface data with similar surface characteristics and under similar imaging conditions.

[1]  A Dunford,et al.  The future of skid resistance? New methods for specification and measurement , 2009 .

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

[3]  Kathleen L. Hancock,et al.  GROUND-BASED IMAGE AND DATA ACQUISITION SYSTEMS FOR ROADWAY INVENTORIES IN NEW ENGLAND - A SYNTHESIS OF HIGHWAY PRACTICE , 2002 .

[4]  Kent A. Stevens,et al.  Slant-tilt: The visual encoding of surface orientation , 1983, Biological Cybernetics.

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

[6]  J. Gibson.  The perception of visual surfaces. , 1950, The American journal of psychology.

[7]  A. R. Allen,et al.  Estimating the size composition of sediment surfaces through image analysis , 2000 .

[8]  Jeffrey A Saunders,et al.  The Effect of Texture Relief on Perception of Slant from Texture , 2003, Perception.

[9]  Kent A. Stevens,et al.  The Visual Interpretation of Surface Contours , 1981, Artif. Intell..

[10]  R Schonfeld,et al.  PHOTO-INTERPRETATION OF PAVEMENT SKID RESISTANCE IN PRACTICE , 1974 .

[11]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[12]  R Sinhal,et al.  How do you compare? Correlation and calibration of skid resistance and road surface friction measurement devices , 2005 .

[13]  Cairney.  Road surface characteristics and crash occurrence: a literature review , 2008 .

[14]  Two-Phase Grain Structures.  Standard Test Methods for Determining Average Grain Size Using Semiautomatic and Automatic Image Analysis 1 , 2004 .

[15]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[16]  P Roe,et al.  Wehner-Schulze machine: First UK experiences with a new test for polishing resistance in aggregates , 2006 .

[17]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Young Choi.  Review of surface texture measurement method for seal design input , 2008 .

[19]  A. U.S.,et al.  Recovering Surface Shape and Orientation from Texture , 2002 .

[20]  T. Poggio.  Vision by man and machine. , 1984, Scientific American.

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

[22]  Ruzena Bajcsy,et al.  Texture gradient as a depth cue , 1976 .

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

[24]  M. K. Chang,et al.  INFLUENCE OF COARSE AGGREGATE SHAPE ON THE STRENGTH OF ASPHALT CONCRETE MIXTURES , 2005 .

[25]  Ramakant Nevatia,et al.  Perception of 3-D Surfaces from 2-D Contours , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  E. Dougherty,et al.  Gray-scale morphological granulometric texture classification , 1994 .

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

[28]  David Croney,et al.  The design and performance of road pavements , 1977 .

[29]  P Cairney,et al.  A Pilot Study of the Relationship Between Macrotexture and Crash Occurrence , 2005 .

[30]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[31]  K. Laws.  Textured Image Segmentation , 1980 .

[32]  Narendra Ahuja,et al.  Shape From Texture: Integrating Texture-Element Extraction and Surface Estimation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  R M Larson,et al.  Tire pavement noise and safety performance , 1996 .

[34]  J.L. Vilaca,et al.  A new machine for acquire pavement texture , 2009, 2009 IEEE International Conference on Computational Cybernetics (ICCC).

[35]  Douglas D. Gransberg,et al.  Quantifying Seal Coat Surface Condition Using Digital Image Processing Based on Information Theory , 2002 .

[36]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[37]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[38]  Ahmed Shalaby,et al.  Image Requirements for Three-Dimensional Measurements of Pavement Macrotexture , 2008 .