Study On Algorithms Of The Pavement Image Crack Detection Based On The Grey System Theory

Road surface crack detection has been one of the main issues of highway care and maintenance after the highway being put into operation. With the sustainable development of global economy, most countries have set off a wave of enthusiasm about developing the infrastructure, in which road-building, as a highlight of the transport sector, has attracted universal attention. As the mileage of completed highway is becoming longer and longer, traditional manual way of road pavement testing is increasingly unable to meet the growing testing workload, neither can it adapt to the testing requirements of the new era. Therefore, researching and developing the automated, high-tech road surface testing equipment and algorithm has become an important task for the development of road traffic industry.In this paper, via referring to a large number of corresponding literatures, we summarise the current crack detection algorithms of the haighway pavement image. Viewing the fact that the current road surface image crack detection algorithms are mainly limited to the traditional theories of the basic image processing techniques, mathematical morphology, neural networks, and wavelet analysis theory, this paper attempts to apply the grey system theory to the road surface image preprocessing and segmentation with an effort to lay a foundation for the follow-up treatment of crack detection.Beginning with the analysis of the mechanical causes of road pavement crack, the paper has clarified the rationality and superiority of applying the grey system theory to solving the problem of pavement image crack detection based on the analysis of the grey characteristics of the pavement crack image, has sumed up the development of road testing equipment and has introduced the basic concept of grey system theory. By analyzing the basic principles of the pavement crack image denoising, filtering, enhancement, and edge detection, we propose ten new kinds of algorithms about the pavement image denoising, filtering, enhancement and edge detection combining with the grey relational analysis, grey entropy, grey prediction theory respectively.Viewing the fact that when applying the Deng's grey relational degree to the image data computation, the denominator in the formula may appear zero, the filtering effect is not very satisfactory, and taking into account the characteristics of the road image data, this paper propose a new grey relational degree model, named grey image relational degree, which selects the part of the data in the neighborhood of image to carry out weighted average operation, and makes full use of the new pixel gray value in the neighborhood window resulting from the current traversal.When the noise density of the road surface image increases, the noise points in the image neighborhood window turn to an extent that can not be ignored when computing the central weighted value. This paper propose that, before pavement image denoising, the noise and non-noise points should be distinguished according to grey relational order of pixel gray value in the neighborhood window. And then for the pixel that is noise point as the center of the neighborhood window, the non-noise pixels around the central pixel in the neighborhood are chosen to carry out the grey relational noise reduction operator, or expand the window to carry out the median filter operator. In a noise-containing pavement image, this paper uses the entropy between the central pixel and each other pixel of the beighborhood as the weighted exponent to compute the weighted average value of all pixels in the neighborhood as the new value of central pixel in the neighborhood to achieve the noise filtered. By sorting the pixel gray value of the image neighborhood window, we divide the pixels of the image into three groups to deal with them respectively:those remain the normal unchanged, those expand the window for median filtering for large noise block, and those only make the noise with a neighborhood containing a small amount of noise operated by grey prediction filtering based on a affine transformation.Through computing the grey relational entropy between the central pixel of the neighborhood window and each adjacent pixel of the neighboerhood, we measured the local edge degree of the neighborhood window, and then searched the threshold value to nake the edge of the pavement image that has been segmented from the background. Aftering calculating the pixel grey entropy values of sixteen kinds of texrure in the image neighborhood, we computed the difference between the maximum and minimum of the gray entropy in order to find out the edge feature of local texture variances, and then set a threshold to make the edge of pavement detected. We made GM(1,1,C) models for the pixel groups with four main texture toward in the image neighborhood window by adding auxiliary point and by supposing the difference of the maximum and minimum of rediduals summation of fitting values of four models be the extent of measure of the current central point,then we set thresholds and extracted the edge of the pavement image.In the image neighborhood window, we selected the part of pixels which are with property different from the central pixel by using the gray image relational degree, and increased the contrast between their average value and the central pixel value in order to improve the effect of image enhancement. Using the grey entropy value of the neighborhood of the image, we constructed the enhancement factor of the image fuzzy local contrast enhancement in order to make the fuzzy local contrast function adaptively enhanced. From eight directions of the current central pixel, pointing to the centre point of the surrounding neighborhood window, we chose the absolute value of difference between the centre poit and the average value of all the pixels in the neighborhood as the original data points, generated auxiliary data points according to the way of using the mean value of pixels adjacent, established a discrete grey prediction model with a fixed end point, made the fitted values of the model as the scale of local contrast enhancement to adaptively adjust the image local contrast, and improved the effect of the pavement image enhancement.This dissertation was from Specialized Research Fund for the Doctoral Program of Higher Education of China:Study on Modeling and Prediction of the Ultimate Bearing Capacity Based on the Generalized Accumulated Generating Operation (NO.200804970005), the National Natural Science Fund Project:Study on Modeling and Prediction of the Short-term Traffic Flow in the Road Network based on Grey Generation Space Modeling (NO.70971103), and the Wuhan Science and Technology Research Projects:Fuzzy Classification of Web Image Semantics based on Evolutionary Optimization(NO.201010621218).