Transportation Informatics: An Image Analysis System for Managing Transportation Facilities-Phase II

摘要

One of the most important tasks in maintaining transportation facilities such as highways and streets is the evaluation of the existing condition. Visual evaluation by human inspectors is subjective in nature, therefore has issues of consistency and the speed and frequency of evaluation is limited due to the manual process. Automated evaluation using modern digital image processing and pattern recognition techniques can increase the efficiency and accuracy and decrease the costs of condition evaluation. Several automated condition evaluation systems have been developed, but these systems commonly require special devices such as strobe light, laser beams, etc, which increase the cost and limit the system to certain applications. In this study, a low cost automatic pavement distress evaluation approach is presented. This method can provide real-time pavement distress detection as well as evaluation results based on color images captured from a camera installed on a survey vehicle. The entire process consists of two main parts: pavement surface extraction followed by pavement distress detection and classification. In the first part, a novel color segmentation method based on a feed forward neural network is employed to separate the road surface from the background. In the second part, a segmentation technique based on probabilistic relaxation is used to separate distress areas from the road surface. The geometrical parameters obtained from the detected distresses are then fed to a neural network based pavement distress classifier in which the defects are classified into different types. Simulation results are given to show that the scheme presented in this report is both effective and reliable on a variety of pavement images.