Underground Pipeline Mapping Based on Dirichlet Process Mixture Model
Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines. In this paper, an automatic pipeline mapping model, the Dirichlet Proces...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9127435/ |
| _version_ | 1818664363773394944 |
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| author | Qingyuan Wu Xiren Zhou Huanhuan Chen |
| author_facet | Qingyuan Wu Xiren Zhou Huanhuan Chen |
| author_sort | Qingyuan Wu |
| collection | DOAJ |
| description | Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines. In this paper, an automatic pipeline mapping model, the Dirichlet Process Pipeline Mapping Model (DPPMM), is introduced with GPR and Global Position System (GPS) data as input. By combining the GPR and GPS the position, direction, depth and size of pipelines could be estimated. The number of buried pipelines in the detection site could be automatically estimated with the benefit of DPPMM, without any prior knowledge. By adopting this model, the probabilities of each survey point belonging to each pipeline are calculated, and the pipeline directions and locations are also estimated. The experimental results demonstrate that this model could obtain more accurate pipeline maps than other state-ofthe-art algorithms in various experimental settings. |
| first_indexed | 2024-12-17T05:31:33Z |
| format | Article |
| id | doaj.art-a579c5242706470793f22d241168234e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:31:33Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a579c5242706470793f22d241168234e2022-12-21T22:01:43ZengIEEEIEEE Access2169-35362020-01-01811811411812410.1109/ACCESS.2020.30054209127435Underground Pipeline Mapping Based on Dirichlet Process Mixture ModelQingyuan Wu0https://orcid.org/0000-0001-5746-148XXiren Zhou1Huanhuan Chen2https://orcid.org/0000-0002-3918-384XSchool of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, UBRI, ChinaSchool of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, UBRI, ChinaSchool of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, UBRI, ChinaUnderground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines. In this paper, an automatic pipeline mapping model, the Dirichlet Process Pipeline Mapping Model (DPPMM), is introduced with GPR and Global Position System (GPS) data as input. By combining the GPR and GPS the position, direction, depth and size of pipelines could be estimated. The number of buried pipelines in the detection site could be automatically estimated with the benefit of DPPMM, without any prior knowledge. By adopting this model, the probabilities of each survey point belonging to each pipeline are calculated, and the pipeline directions and locations are also estimated. The experimental results demonstrate that this model could obtain more accurate pipeline maps than other state-ofthe-art algorithms in various experimental settings.https://ieeexplore.ieee.org/document/9127435/Ground penetrating radar (GPR)pipeline mappingclusteringnonparametric Bayesian model |
| spellingShingle | Qingyuan Wu Xiren Zhou Huanhuan Chen Underground Pipeline Mapping Based on Dirichlet Process Mixture Model IEEE Access Ground penetrating radar (GPR) pipeline mapping clustering nonparametric Bayesian model |
| title | Underground Pipeline Mapping Based on Dirichlet Process Mixture Model |
| title_full | Underground Pipeline Mapping Based on Dirichlet Process Mixture Model |
| title_fullStr | Underground Pipeline Mapping Based on Dirichlet Process Mixture Model |
| title_full_unstemmed | Underground Pipeline Mapping Based on Dirichlet Process Mixture Model |
| title_short | Underground Pipeline Mapping Based on Dirichlet Process Mixture Model |
| title_sort | underground pipeline mapping based on dirichlet process mixture model |
| topic | Ground penetrating radar (GPR) pipeline mapping clustering nonparametric Bayesian model |
| url | https://ieeexplore.ieee.org/document/9127435/ |
| work_keys_str_mv | AT qingyuanwu undergroundpipelinemappingbasedondirichletprocessmixturemodel AT xirenzhou undergroundpipelinemappingbasedondirichletprocessmixturemodel AT huanhuanchen undergroundpipelinemappingbasedondirichletprocessmixturemodel |