Grade evaluation of road cracks is one of the basic tasks in highway maintenance. At present

line array camera is mainly used for road image acquisition by relevant departments. Given that the recognition of road crack image is affected by many factors (such as projection of trees and vehicles

illumination changes

grease

branches and straw

and various types of garbage)

the accuracy of the automatic identification of crack is reduced. Thus

an artificial method is always used to evaluate the road grade. In this paper

a new method of identifying the road crack image is proposed. Given the large size of the collected image and problem posed by uneven illumination

the image is initially divided into many blocks

and pretreatment with CV model is used to process each block to obtain preliminary segmentation results. Cracks of linear array CCD images are identified by the following features:1) cracks occupy a small portion of the patch

2) cracks have poor continuity in these images

3) the ratio of crack width to length is small

and 4) the same trends of cracks are basically consistent. To employ the last two characteristics

we use ellipse fitting method in calculating the direction of the preliminary test results

and these areas are divided into four categories. In each category

the location of the center of mass for each region is calculated

and a vector table between the center of mass tables is established. A recursive algorithm is designed to calculate the collinearity

and crack-detection results are obtained. The accurate cracks are obtained by iteratively solving in the original image. A total of 100 images containing road cracks are selected from 2 000 road images. According to the serial numbers

5 groups at equal intervals are taken out of the images that contain no cracks. Thus

the data sets are constructed with these 200 images in each group. The performance of the algorithm is evaluated by the method of classification index statistics. True positives

false positives

false negatives

and true negatives reached more than 95%

and the execution time of road crack detection and extraction is approximately 1 minute. Experimental results show that the algorithm not only can identify the cracks effectively but can also overcome the negative interference of various factors. Thus

this algorithm has potential for practical implementation.