Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes

Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of intelligent transportation and promote the developm...

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Autores principales:	Ziyang Wang, Lin Yang, Yehua Sheng, Mi Shen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	road scene point clouds pole-like objects point cloud classification random forest model multiscale fusion Science Q
Acceso en línea:	https://doaj.org/article/a01c54f8799d4d0095cfb7e4e43b2012
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id	oai:doaj.org-article:a01c54f8799d4d0095cfb7e4e43b2012
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spelling	oai:doaj.org-article:a01c54f8799d4d0095cfb7e4e43b20122021-11-11T18:55:05ZPole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes10.3390/rs132143822072-4292https://doaj.org/article/a01c54f8799d4d0095cfb7e4e43b20122021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4382https://doaj.org/toc/2072-4292Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of intelligent transportation and promote the development of high-precision maps. However, available algorithms have the problems of incomplete extraction and the low recognition accuracy of pole-like objects. In this paper, we propose a segmentation method of pole-like objects under geometric structural constraints. As for classification, we fused the classification results at different scales with each other. First, the point cloud data excluding ground point clouds were divided into voxels, and the rod-shaped parts of the pole-like objects were extracted according to the vertical continuity. Second, the regional growth based on the voxel was carried out based on the rod part to retain the non-rod part of the pole-like objects. A one-way double coding strategy was adopted to preserve the details. For spatial overlapping entities, we used multi-rule supervoxels to divide them. Finally, the random forest model was used to classify the pole-like objects based on local- and global-scale features and to fuse the double classification results under the different scales in order to obtain the final result. Experiments showed that the proposed method can effectively extract the pole-like objects of the point clouds in the road scenes, indicating that the method can achieve high-precision classification and identification in the lightweight data. Our method can also bring processing inspiration for large data.Ziyang WangLin YangYehua ShengMi ShenMDPI AGarticleroad scene point cloudspole-like objectspoint cloud classificationrandom forest modelmultiscale fusionScienceQENRemote Sensing, Vol 13, Iss 4382, p 4382 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	road scene point clouds pole-like objects point cloud classification random forest model multiscale fusion Science Q
spellingShingle	road scene point clouds pole-like objects point cloud classification random forest model multiscale fusion Science Q Ziyang Wang Lin Yang Yehua Sheng Mi Shen Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
description	Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of intelligent transportation and promote the development of high-precision maps. However, available algorithms have the problems of incomplete extraction and the low recognition accuracy of pole-like objects. In this paper, we propose a segmentation method of pole-like objects under geometric structural constraints. As for classification, we fused the classification results at different scales with each other. First, the point cloud data excluding ground point clouds were divided into voxels, and the rod-shaped parts of the pole-like objects were extracted according to the vertical continuity. Second, the regional growth based on the voxel was carried out based on the rod part to retain the non-rod part of the pole-like objects. A one-way double coding strategy was adopted to preserve the details. For spatial overlapping entities, we used multi-rule supervoxels to divide them. Finally, the random forest model was used to classify the pole-like objects based on local- and global-scale features and to fuse the double classification results under the different scales in order to obtain the final result. Experiments showed that the proposed method can effectively extract the pole-like objects of the point clouds in the road scenes, indicating that the method can achieve high-precision classification and identification in the lightweight data. Our method can also bring processing inspiration for large data.
format	article
author	Ziyang Wang Lin Yang Yehua Sheng Mi Shen
author_facet	Ziyang Wang Lin Yang Yehua Sheng Mi Shen
author_sort	Ziyang Wang
title	Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
title_short	Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
title_full	Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
title_fullStr	Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
title_full_unstemmed	Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes
title_sort	pole-like objects segmentation and multiscale classification-based fusion from mobile point clouds in road scenes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a01c54f8799d4d0095cfb7e4e43b2012
work_keys_str_mv	AT ziyangwang polelikeobjectssegmentationandmultiscaleclassificationbasedfusionfrommobilepointcloudsinroadscenes AT linyang polelikeobjectssegmentationandmultiscaleclassificationbasedfusionfrommobilepointcloudsinroadscenes AT yehuasheng polelikeobjectssegmentationandmultiscaleclassificationbasedfusionfrommobilepointcloudsinroadscenes AT mishen polelikeobjectssegmentationandmultiscaleclassificationbasedfusionfrommobilepointcloudsinroadscenes
_version_	1718431662655668224

Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes

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