Automatic Road Markings Extraction, Classification and Vectorization From Mobile Laser Scanning Data

Pan, Yong; Wang, Jinhu; Li, S.; Yang, Haiying

doi:10.5194/isprs-archives-xlii-2-w13-1089-2019

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…There are two main groups of methods for road marking extraction: bottom-up and top-down methods [16]. Bottom-up methods start with low-level features such as intensity thresholds to differentiate road markings from their surroundings [17][18][19]. While effective, they are sensitive to data quality.…”

Section: A Road Boundary and Marking Extraction From Mls Pointmentioning

confidence: 99%

Precision Inverse Modeling of Highway Pavements Based on Standardized Alignment

Ma,

Zhu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Reconstruction and expansion, as well as asset management, of highways necessitate the development of a current and highly precise 3D pavement model. Current inverse modeling methods with point clouds are laborious, time-consuming, and limited in precision. This paper introduces an alternative framework for parametric inverse procedural modeling of highway pavement with standardized alignments seamlessly integrated with off-the-shelf modeling software. It comprises three key steps. (1) Extraction of Highway Pavement Boundaries and Lane Markings: Initially, we combine grid-based and modeldriven methods, followed by line structure-based clustering, to accurately generate road centerlines and layouts. (2) Road Centerline Generation: The centerline, derived from lane markings, informs highway alignments and parameters based on geometric characteristics such as curvature and slope. We utilize cost functions to facilitate this process. (3) Novel Inverse Procedural Assembly: This innovative step integrates off-the-shelf modeling software. This approach involves extracting vector lines from point clouds and applying constraints at pivotal points on highway pavement cross-sections. Our focus is on refined component-level modeling, allowing for the assembly of diverse highway elements. This method significantly reduces human intervention and achieves high precision. In tests on two highway datasets from Sichuan Province, China, our method achieved excellent results. It attained an average correctness of 98.63% and completeness of 99.66% within a 10 cm error margin. A comparison with the intersection point method indicated minimal errors, with maximum values below 1.2%. The resultant 3D highway pavement model is modular and highly accurate at the centimeter level.

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Section: A Road Boundary and Marking Extraction From Mls Pointmentioning

confidence: 99%

Precision Inverse Modeling of Highway Pavements Based on Standardized Alignment

Ma,

Zhu,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Intensity image- and point-based approaches are the two primary methods of extracting pavement markings using LiDAR point clouds. Intensity images generated from the color characteristics of point clouds have been investigated to extract pavement markings ( 10 – 12 ); however, converting point clouds to intensity images causes loss of spatial information. In point-based methods, height, shape, and intensity characteristics have been explored to distinguish pavement markings based on surrounding objects ( 13 , 14 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Point-Based Visual Status Evaluation of Worn Pavement Markings Based on a Feature-Binary-PointNet Network and Shape Descriptors Using LiDAR Point Clouds: A Case Study of an Expressway

Wang,

Cao,

Zhang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Inspecting the visual wear status of pavement markings is important to guide the maintenance plans of roads and ensure traffic safety. This study proposes a point-based visual status evaluation system for pavement markings using light detection and ranging (LiDAR) technology. The system includes a feature-binary-PointNet (FBP) network to accurately extract pavement markings from raw point clouds. Furthermore, multi-shape descriptors are constructed to efficiently identify different types of pavement markings, and a four-level wear-grading system is established based on two indices (point-based visual wear area and wear area proportion) of each pavement marking. The proposed method was tested using the mobile laser scanning technique considering a case study of an expressway. The proposed FBP network performed better than the existing state-of-the-art networks in extracting the pavement markings, with accuracy, recall, precision, and mean intersection over union scores of 97.04, 84.42, 91.11, and 79.36%, respectively. The main types of expressway markings were rapidly identified, and maintenance advice was provided based on the two computed indices and the wear grade of each marking. The study findings can guide managing agencies in allocating the maintenance budget for fixing highly defective road sections.

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