Model-Driven Precise Degradation Analysis Method of Highway Marking Using Mobile Laser Scanning Point Clouds

Abstract

Highway markings (HMs) are representative elements of inventory digitalization in highway scenes. The accurate position, semantics, and maintenance information of HMs provide significant support for the intelligent management of highways. This article presents a robust and efficient approach for extracting, reconstructing, and degrading analyzing HMs in complex highway scenes. Compared with existing road marking extraction methods, not only can extract HMs in presence of wear and occlusion from point clouds, but we also perform a degradation analysis for HMs. First, the HMs candidate area is determined accurately by sophisticated image processing. Second, the prior knowledge of marking design rules and edge-based matching model that leverages the standard geometric template and radiometric appearance of HMs is used for accurately extracting and reconstructing solid lines and nonsolid markings of HMs, respectively. Finally, two degradation indicators are constructed to describe the completeness of the marking contour and consistency within the marking. Comprehensive experiments on two existing highways revealed that the proposed methods achieved an overall performance of 95.4% and 95.4% in the recall and 93.8% and 95.5% in the precision for solid line and nonsolid line markings, respectively, even with imperfect data. Meanwhile, a database can be established to facilitate agencies' efficient maintenance.