LOW-COST VIDEO IMAGE PROCESSING SYSTEM FOR EVALUATING PAVEMENT SURFACE DISTRESS

Most pavement condition surveys use rating systems in which pavement distress is measured by type, extent, and severity. Rating either bituminous or portland cement concrete pavements by the pavement condition rating (PCR) or similar systems is tedious and time-consuming. In most instances, distress measurement is subjective, which affects the actual rating. Improved data collection and data processing methods are thus needed to expedite pavement evaluation that can be used as input in pavement management systems. Recent advances in computer technology now permit the identification and quantification of distress types that can be measured by width, length, or area by automatic analyses of images captured by a microcomputer from video or film recordings. The development of a low-cost system for video image pavement distress analysis is described; the system allows the identification, classification, and quantification of commonly occurring pavement distress types in terms of severity and extent. Once pavement distress is identified, quantified, and classified, the system can be combined with rating procedures (such as PCR) to obtain a quantitative measure of pavement condition. Hardware and software characteristics of the system are described in detail. Diagrams showing the connection of system components and procedural steps leading to the calculation of a pavement rating are also included. Distress identification and classification is currently limited to distress types that can be quantified by width, length, geometry, or area covered by the distress.

[1]  Paul Suetens,et al.  Critical Review Of Visual Inspection , 1985, Photonics West - Lasers and Applications in Science and Engineering.

[2]  Edward J. Delp,et al.  Adaptive gray scale mapping to reduce registration noise in difference images , 1986, Comput. Vis. Graph. Image Process..

[3]  B. R. Hunt,et al.  Digital Image Restoration , 1977 .

[4]  A. Venetsanopoulos,et al.  Nonlinear order statistic filters for image filtering and edge detection , 1986 .

[5]  J A Acosta,et al.  IMPLEMENTATION OF THE VIDEO IMAGE PROCESSING TECHNIQUE FOR EVALUATING PAVEMENT SURFACE DISTRESS IN THE STATE OF OHIO. FINAL REPORT , 1993 .

[6]  Azriel Rosenfeld,et al.  ";Expert" vision systems: Some issues , 1986, Computer Vision Graphics and Image Processing.

[7]  S Inoué,et al.  Video image processing greatly enhances contrast, quality, and speed in polarization-based microscopy , 1981, The Journal of cell biology.

[8]  Ahmed S. Abutableb Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

[9]  Thomas S. Huang,et al.  Picture processing and digital filtering, 2nd edition. , 1979 .

[10]  Robert A. Hummel,et al.  Image Enhancement by Histogram transformation , 1975 .

[11]  C. K. Chow,et al.  X-ray image subtraction by digital means , 1973 .

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

[13]  Stephen G. Ritchie Closure of "Digital Imaging Concepts and Applications in Pavement Management" , 1990 .

[14]  Victor T. Tom,et al.  Adaptive Filter Techniques For Digital Image Enhancement , 1985, Photonics West - Lasers and Applications in Science and Engineering.

[15]  John F. Walkup,et al.  Nonlinear Image Restoration , 1985, Photonics West - Lasers and Applications in Science and Engineering.

[16]  Jim Baker,et al.  VIDEO IMAGE DISTRESS ANALYSIS TECHNIQUE FOR IDAHO TRANSPORTATION DEPARTMENT PAVEMENT MANAGEMENT SYSTEM , 1987 .