On Road Defects Detection and Classification

The road pavement condition is a ected by various impacts such as trucks, deicing reagents, base erosion, etc. After some time on the road surface occur defects. Engineers are commonly used to collect pavement surface distress data, during periodic road surveys, but it takes a lot of time and manpower. In this paper, we present our automatic defects detection and classi cation on road pavement method. We suggest the novel approach to detect the di erent types of defects such as rupture of the road edge, potholes, subsidence depressions. Images of road pavement have been preprocessed to noise lter and smooth, then classi ed two class defects/ non defects, next step to process with defects class. We propose three main steps in our approach. First step is to detect defect position (ROI). In the second step, defect is described by its features. The last step is to classify defect each using these di erent defect features such as Chain Code Histogram, Hu-Moments, size of defect region(width and length, area) and histogram of image. In our approach the following algorithms have been used: Markov Random Fields for image segmentation, Random Forests algorithm for data classi cation. Data collection on real roads, real-time processing and comparison with other algorithms, analyzes the advantages and disadvantages of each methods.