An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds
In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes a...
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MDPI AG
2024-11-01
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author | Jingxu Chen Qiru Cao Mingzhuang Hua Jinyang Liu Jie Ma Di Wang Aoxiang Liu |
author_facet | Jingxu Chen Qiru Cao Mingzhuang Hua Jinyang Liu Jie Ma Di Wang Aoxiang Liu |
author_sort | Jingxu Chen |
collection | DOAJ |
description | In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid method with Bayesian optimization for road edge extraction in highway systems. The hybrid method combines the strengths of 2D feature images and 3D spatial characteristics while also automatically tuning the hyperparameter combination using Bayesian optimization. The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. Later, the point cloud dataset of national highways in Henan Province, China, is taken as the case study to evaluate the performance of the proposed method against three benchmark methods in two typical road scenarios: straight and curved edges. Experimental results show that the proposed method outperforms the benchmarks in detection quality and accuracy. It can serve as a decision-making tool to complement traditional manual road surveying, enabling efficient and automated road edge extraction in highway systems. |
format | Article |
id | doaj-art-6ac17dcead324f68a76359c1ccc1a9cc |
institution | Kabale University |
issn | 2079-8954 |
language | English |
publishDate | 2024-11-01 |
publisher | MDPI AG |
record_format | Article |
series | Systems |
spelling | doaj-art-6ac17dcead324f68a76359c1ccc1a9cc2024-11-26T18:23:22ZengMDPI AGSystems2079-89542024-11-01121148010.3390/systems12110480An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point CloudsJingxu Chen0Qiru Cao1Mingzhuang Hua2Jinyang Liu3Jie Ma4Di Wang5Aoxiang Liu6School of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaCollege of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaTransportation Development Center of Henan Province, Zhengzhou 450003, ChinaTransportation Development Center of Henan Province, Zhengzhou 450003, ChinaIn transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid method with Bayesian optimization for road edge extraction in highway systems. The hybrid method combines the strengths of 2D feature images and 3D spatial characteristics while also automatically tuning the hyperparameter combination using Bayesian optimization. The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. Later, the point cloud dataset of national highways in Henan Province, China, is taken as the case study to evaluate the performance of the proposed method against three benchmark methods in two typical road scenarios: straight and curved edges. Experimental results show that the proposed method outperforms the benchmarks in detection quality and accuracy. It can serve as a decision-making tool to complement traditional manual road surveying, enabling efficient and automated road edge extraction in highway systems.https://www.mdpi.com/2079-8954/12/11/480highway systemspoint cloudsroad edge extractionBayesian optimizationhyperparameter combination |
spellingShingle | Jingxu Chen Qiru Cao Mingzhuang Hua Jinyang Liu Jie Ma Di Wang Aoxiang Liu An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds Systems highway systems point clouds road edge extraction Bayesian optimization hyperparameter combination |
title | An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds |
title_full | An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds |
title_fullStr | An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds |
title_full_unstemmed | An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds |
title_short | An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds |
title_sort | autotuning hybrid method with bayesian optimization for road edge extraction in highway systems from point clouds |
topic | highway systems point clouds road edge extraction Bayesian optimization hyperparameter combination |
url | https://www.mdpi.com/2079-8954/12/11/480 |
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