Positioning and Detection of Rigid Pavement Cracks Using GNSS Data and Image Processing

Abstract

Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. To assess pavement damages conventionally, manual inspection are performed by trained inspectors. This can be time-consuming and a source of risk for inspectors. Moreover, manual inspection can highly affected by the state of mind of inspectors. To overcome such problems, image based inspection using smartphone camera combined with image processing methods can be used. This combination is relatively cheaper and easier to use. This research proposes an automatic crack detection and mapping program for rigid pavement which can automate visual inspection process. The program consists of various image processing techniques that are used to identify and detect cracks from images. Detected cracks are defined in a pixel coordinate system. Cracks coordinates are converted from pixel coordinates to global coordinates in order to compute their lengths using Global Navigation Satellite Systems (GNSSs) data. The performance of the program was assessed with field study. Cracks quantification process is performed to determine crack lengths and areas. The results show that the precision, recall and accuracy values for the program’s image processing algorithm are 57.00%, 98.81% and 65.22% respectively.