Development of Digital Image-Processing Algorithm To Compute Unified Crack Index for Salt Lake City

This paper presents the recent efforts in developing an image processing algorithm for computing a unified pavement crack index for Salt Lake City. The pavement surface images were collected using a digital camera mounted on a van. Each image covers a pavement area of 2.13 m (7 ft) X 1.52 m (5 ft), taken at every 30-m (100-ft) station. The digital images were then transferred onto a 1-gigabyte hard disk from a set of memory cards each of which can store 21 digital images. Approximately 1,500 images are then transferred from the hard disk to a compact disc. The image-processing algorithm, based on a variable thresholding technique, was developed on a personal computer to automatically process pavement images. The image is divided into 140 smaller tiles, each tile consisting of 40 X 40 pixels. To measure the amount of cracking, a variable threshold value is computed based on the average gray value of each tile. The program then automatically counts the number of cracked tiles and computes a unified crack in...

[1]  L. Hamilton.  217-249 inRegression with Graphics: A Second Course in Applied Statistics , 1991 .

[2]  Allen B. Downey,et al.  Automated analysis of pavement distress data , 1991 .

[3]  S A Velinsky,et al.  Histogram‐Based Approach for Automated Pavement‐Crack Sensing , 1992 .

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

[5]  Azriel Rosenfeld,et al.  Thresholding Using Relaxation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  M H Mohajeri,et al.  ARIA: an operating system of pavement distress diagnosis by image processing , 1991 .

[7]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Hosin Lee.  ACCURACY, PRECISION, REPEATABILITY, AND COMPATIBILITY OF THE PAVEDEX PAS 1 AUTOMATED DISTRESS MEASURING DEVICE , 1991 .

[9]  Allen B. Downey,et al.  ANALYSIS OF SEGMENTATION ALGORITHMS FOR PAVEMENT DISTRESS IMAGES , 1993 .

[10]  Robert L. Lytton,et al.  DETECTION OF THIN CRACKS ON NOISY PAVEMENT IMAGES , 1991 .

[11]  Robert L. Lytton,et al.  Flexible pavement distress evaluation using image analysis , 1991 .

[12]  Edward R. Dougherty,et al.  Matrix Structured Image Processing , 1987 .

[13]  H Lee,et al.  New crack-imaging procedure using spatial autocorrelation function , 1994 .

[14]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[15]  S G Ritchie,et al.  Pavement Image Processing Using Neural Networks , 1991 .

[16]  Haris N. Koutsopoulos,et al.  METHODS AND ALGORITHMS FOR AUTOMATED ANALYSIS OF PAVEMENT IMAGES , 1991 .

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

[18]  Josef Kittler,et al.  Threshold selection based on a simple image statistic , 1985, Comput. Vis. Graph. Image Process..

[19]  W R Hudson,et al.  FUTURE DIRECTIONS AND NEED FOR INNOVATION IN PAVEMENT MANAGEMENT , 1995 .

[20]  H Lee.  Standardization of distress measurements for the network-level pavement management system , 1991 .

[21]  Stephen G. Ritchie,et al.  DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR AUTOMATED PAVEMENT EVALUATION , 1991 .

[22]  Allen B. Downey,et al.  Primitive-Based Classification of Pavement Cracking Images , 1993 .

[23]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..