Investigations of the road pavement surface conditions using MATLAB image processing

In view of the increase in the number of vehicles, there has been a need to devise new ways to conduct rapid assessments of the pavement efficiency and thus, improve road performance. Road performance assessment can be carried out in a variety of ways and can predict the future deterioration of the pavement efficiency. Commonly, pavement condition can be estimated on four portions i.e. surface distress, riding quality, skid resistance in addition to structural capacity. In a pavement maintenance management system, the assessment of pavement surface failures is one of the significant duties for improving maintenance and rehabilitation strategies. Different image analysis and processing techniques using MATLAB programming language code have been sophisticated for discovering of distresses like patches, potholes, cracks etc. on the pavement surface, which is called Automatic Evaluation Of Pavement (AEOP). The goal of this paper is to provide an overview of application of the different image processing techniques for discovering and categories of pavement distresses. The code was trained on more than 360 images to increase the efficiency and accuracy of the diagnosis and was then examined on 40 images and gaving results with 77.5% accuracy.