Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor-Reference-Cited by-同舟云学术

Network-Level Guardrail Extraction Based on 3D Local Features from Mobile LiDAR Sensor

Published:2022-11 Issue:6 Volume:36 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Hou Qing¹,Ai Chengbo²^ORCID,Boudreau Neil³^ORCID

Affiliation:

1. Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts, Amherst, MA 01003.

2. Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Massachusetts, Amherst, MA 01003 (corresponding author). ORCID: .

3. Assistant Administrator for Traffic and Safety, Massachusetts Dept. of Transportation, Highway Div., Boston, MA 02116. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0001049

Reference47 articles.

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2. Adam C. R. Schubert N. Mattern and G. Wanielik. 2011. “Probabilistic road estimation and lane association using radar detections.” In Proc. 14th Int. Conf. on Information Fusion 1–8. New York: IEEE.

3. Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion

4. Road Environment Semantic Segmentation with Deep Learning from MLS Point Cloud Data

5. Segmentation and recognition of roadway assets from car-mounted camera video streams using a scalable non-parametric image parsing method

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