<title>Automatic inspection of road surfaces</title>

Abstract

Traditional inspections of road surfaces for the condition assessment and for locating cracks are time-consuming, expensive and can prove to be dangerous. What is ideally required would be a fully equipped automated inspecting vehicle capable of high precision location and characterization of road surface cracks over the width of the road (single pass). We propose an automatic crack monitoring system (akin to HARRIS - UK) with the video-based subsystem substituted by Global Positioning Systems for more accurate positioning. Besides, our technique avoids the storage of large volumes of scanned images of 'acceptable' road surface conditions. A pulse coupled neural network (PCNN) is used as a preprocessor for each scanned image to detect cracks while another PCNN segments this image to characterize identified defects. The latter image is then stored as binary image along with the GPS data. The type of cracks is later identified (offline) from the recorded binary images. This mode of data collection leads to a more accurate, less costly and faster automated system. Our results for road surface (concrete and bituminous) images reveal the suitability of this novel technique for a fully automated road inspection system for crack identification and characterization.