Application Of Image System For Road Pavement Distress Test

Pavement distress detection plays an important role in the highway management and maintenance. The traditional manual methods can't meet the needs of pavement distress detection now, because they have the following shortcomings such as time-consuming, non-precision, dangerous, costly and also affecting transportation. As the quick expend of computer and photoelectricity technology, the application of road non-destructive test for road pavement distress test is necessary.This paper investigates the application of the advanced digital image system in asphalt expressway pavement test, which uses high rapid and precise cameras to screen pavement images, with fast processing of computers, to get distress information. However, because of highway pavement images' complexity and diversity, and also distress information's weak-signal, it is not easy to get efficient detection algorithms.In this paper, lots of algorithm of digital image processing is analyzed such as the efficient pre-processing algorithm of highway pavement image which integrates manifold technologies of image enhancement and gray scale image processing, the ways of binarizing image and binary image processing, a large number of classical algorithms of edge detection. Upon the repeating tests of vast pavement images, the author gets a series of effective algorithms for analyzing pavement distress automatically. BP artificial neural network is also analyzed in this paper, which provides a more accurate way of digital image processing, and gets a platform based on BP artificial neural network.Finally, the author has compiled a set of software with OOP (Object Oriented Program) method, which is compiled by C# on the platform of .NET. Using the software, we have checked up the algorithm. Applying this software, the user can look on every image processed step by step and the gray diagram analysis of the image. In addition, the system in this research can be applied to analyze asphalt pavement distress automatically, and provide scientific ground for the highway management and maintenance department.