Instance-Based Clustering of Road Markings with Wear and Occlusion from Mobile Lidar Data-Reference-Cited by-同舟云学术

Instance-Based Clustering of Road Markings with Wear and Occlusion from Mobile Lidar Data

Published:2024-07 Issue:4 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Jung Jaehoon¹^ORCID,Che Erzhuo²,Olsen Michael J.³,Parrish Christopher E.⁴^ORCID,Turkan Yelda⁵^ORCID,Yoo Suhong⁶^ORCID

Affiliation:

1. Assistant Professor, Dept. of Urban Engineering, Gyeongsang National Univ., 501 Jinju-daero, Jinju, Gyeongnam 52828, South Korea (corresponding author). ORCID: .

2. Assistant Professor and Senior Research, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearney Hall, Corvallis, OR 97331.

3. Professor, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearney Hall, Corvallis, OR 97331.

4. Professor, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearney Hall, Corvallis, OR 97331. ORCID: .

5. Associate Professor, School of Civil and Construction Engineering, Oregon State Univ., 101 Kearney Hall, Corvallis, OR 97331. ORCID: .

6. Assistant Professor, Dept. of Drone and GIS Engineering, Namseoul Univ., 91, Daehak-ro, Seonghwan-eup, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31020, South Korea. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5728

Reference35 articles.

1. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

2. Efficient segment-based ground filtering and adaptive road detection from mobile light detection and ranging (LiDAR) data

3. Pavement Marking Retroreflectivity Estimation and Evaluation using Mobile Lidar Data

4. Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds

5. Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection