Traffic Sign Extraction from Mobile LiDAR Point Cloud

Ahn, Yushin; Munjy, Riadh; Li, Ziyuan; San Jose State University. College of Business. Mineta Transportation Institute

doi:10.31979/mti.2024.2354

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Traffic Sign Extraction from Mobile LiDAR Point Cloud

2024-06-01
By Ahn, Yushin ; Munjy, Riadh ; Li, Ziyuan

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English

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Creators:

Ahn, Yushin ; Munjy, Riadh ; Li, Ziyuan

Ahn, Yushin ; Munjy, Riadh ; Li, Ziyuan Less -
Corporate Creators:

San Jose State University. College of Business. Mineta Transportation Institute
Corporate Contributors:

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Cloud Computing Data Geographic Information Systems Laser Radar Mobile Applications Mobile LiDAR MTLS Point Cloud Traffic Signs Transportation
Publication/ Report Number:

24-07 ; CA-MTI-2354

24-07 ; CA-MTI-2354 Less -
DOI:

https://doi.org/10.31979/mti.2024.2354
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

Mineta Transportation Institute
Abstract:

The extraction of traﬃc signs from Mobile Light Detection and Ranging (LiDAR) point cloud data has become a focal point in transportation research due to the increasing integration of LiDAR technologies. LiDAR, a remote sensing technology, captures detailed three-dimensional point cloud data, oﬀering a comprehensive view of the surrounding environment. Mobile LiDAR systems mounted on vehicles enable eﬃcient data collection, particularly for large-scale road networks. This study aims to develop and reﬁne techniques for extracting traﬃc signs from Mobile LiDAR point cloud data, essential for enhancing road safety, navigation systems, and intelligent transportation solutions. By leveraging LiDAR technology, new possibilities for automating traﬃc sign recognition and mapping emerge. The research focuses on detecting traﬃc signs using Mobile LiDAR point cloud data, employing an intensity-based sign extraction method to identify traﬃc signs, traﬃc signals, and other retro-reﬂective objects. The workﬂow involves managing LiDAR Aerial Survey (LAS) datasets, including tasks such as merging/splitting, gridding, and detecting high-intensity features. Identiﬁed signs are visualized in Google Earth Pro, facilitating their display in Geographic Information Systems (GIS). Furthermore, the study explores point density analysis, establishing connections with potential grid resolutions for additional extraction or analysis, such as road condition assessments or crack detection.
Format:

PDF
Funding:

ZSB12017-SJAUX
Collection(s):

University Transportation Centers
Main Document Checksum:

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urn:sha-512:77a98721b378d7db107e76f35604949547d44f28924f1fce78e9fd4527eeadb1717829aef2925bd1a74494f68271493813d813741b8f9fc046b79e1b33129005
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https://rosap.ntl.bts.gov/view/dot/75693/dot_75693_DS1.pdf
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Traffic Sign Extraction from Mobile LiDAR Point Cloud

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