S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 32, Number 11(4) (2020)
Copyright(C) MYU K.K.
pp. 4005-4017
S&M2390 Technical Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3118
Published: November 30, 2020

Expressway Guardrail Extraction and Classification Based on Slice Method [PDF]

Ming Huang, Chuanli Zhou, Pengpeng Huo, and Zhiqun Wu

(Received September 27, 2020; Accepted November 16, 2020)

Keywords: expressway, point cloud, guardrail recognition, type distinguishment

Guardrails are an important ancillary facility of expressways. Accurate and effective identification of guardrails from road scenes plays an important role in high-precision map construction, automatic driving, road maintenance, and so on. At present, there are few research methods on guardrail recognition in highway scenes, and the existing methods have some defects such as low recognition accuracy and inability to distinguish guardrail types. In this paper, a point cloud of an expressway scene obtained by mobile laser scanning is taken as the research object. On the basis of the binary coding voxelization segmentation method, the clustering slicing method is used to recognize the guardrail target in the expressway scene according to the characteristics of the guardrail. In addition, a method for distinguishing the type of guardrail based on its cross-section characteristics is proposed to distinguish steel and concrete guardrails in a highway. The experimental results show that the proposed method can effectively identify the guardrail target in the point cloud of an expressway scene, and has high efficiency and accuracy.

Corresponding author: Pengpeng Huo


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Cite this article
Ming Huang, Chuanli Zhou, Pengpeng Huo, and Zhiqun Wu, Expressway Guardrail Extraction and Classification Based on Slice Method, Sens. Mater., Vol. 32, No. 11, 2020, p. 4005-4017.



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