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.
As a basic asset of highways, guardrails are essential objects in the digital modeling of highways. Therefore, generating the vectorial 3D trajectory of a guardrail from mobile laser scanning (MLS) point clouds is required for real digital modeling. However, most methods limit straight-line guardrails without considering the continuity and accuracy of the guardrails in turnoff and bend areas; thus, a completed 3D trajectory of a guardrail is not available. We use RANDLA-Net for extracting guardrails as preprocessing of MLS point clouds. We perform a region growth strategy based on linear constraints to obtain correct instantiations and a forward direction. The improved Douglas– Puke algorithm is used to simplify the center points of guardrail, and the 3D trajectory of every guardrail can be vectorized using cubic spline curve fitting. The proposed approach is validated on two 3-km case data sets that can completely instantiate MLS point clouds with remarkable effects. Quantitative evaluations demonstrate that the proposed guardrail instantiation algorithm achieves an overall precision and recall of 98.80% and 97.5%, respectively. The generated 3D trajectory can provide a high-precision design standard for the 3D modeling of the guardrail and has been applied to a long highway scene.
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