Automatic road-marking detection and measurement from laser-scanning 3D profile data

Abstract

Abstract Automatic road-marking detection and measurement have great significance for pavement maintenance and management. Laser-scanning 3D profile data provide a new way of road-marking detection and measurement with an elevation accuracy of about 0.25 mm. This paper presents an automatic road-marking detection and measurement method that uses laser scanning of 3D pavement data. The elevation characteristics and geometric statistics that characterize road markings have been fully analyzed using 3D data. The first step was to use a specially designed step-shaped operator to convolve profile data to identify the regions of suspected marking edges at the profile level, which helps reduce the influence of other pavement factors, including crosswise-slope information, cracks, and rutting. Next, by combining the geometric characteristics of the road-marking region and the continuity of the convolution features at image level, the regions of suspected 3D road markings were extracted. Third, a convolutional neural network was introduced to distinguish real-marking data more clearly. Finally, the three-dimension measurement information was extracted from the detected region and from elevation information. Road-marking recognition experiments were then conducted based on real measured 3D data. The detection accuracies were all greater than 90.8% for 4178 test samples from five road sections with different kinds of road markings. Furthermore, the repeatability of multiple measurement results for road-marking elevations from two selected road sections was about 95%, and the correlation of the obtained road-marking elevations with manually measured elevations was about 85.36% for 200 measurement points.