Location monitoring approach of underground pipelines using time-sequential images
The location monitoring of underground pipelines is of utmost significance as it helps the effective management and maintenance of the pipelines, and facilitates the planning of nearby projects, preventing damage to the pipelines. However, currently there is a serious lack of data on the locations o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-04-01
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| Series: | Underground Space |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2467967423001113 |
| _version_ | 1797377824794869760 |
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| author | Haoruo Xu Lei He Yuyang Chu Junchen He Huaiguang Xiao Chengmeng Shao |
| author_facet | Haoruo Xu Lei He Yuyang Chu Junchen He Huaiguang Xiao Chengmeng Shao |
| author_sort | Haoruo Xu |
| collection | DOAJ |
| description | The location monitoring of underground pipelines is of utmost significance as it helps the effective management and maintenance of the pipelines, and facilitates the planning of nearby projects, preventing damage to the pipelines. However, currently there is a serious lack of data on the locations of underground pipelines. This paper proposes an image-based approach for monitoring the locations of underground pipelines by combing deep learning and visual-based reconstruction. The proposed approach can build the monitoring model for underground pipelines and characterize their locations through their centroid curve. Its advantages are: (1) simplicity: it only requires time-sequential images of the inner walls of underground pipelines; (2) clarity: the location model and the location curve of underground pipelines can be provided quickly; (3) robustness: it can cope with some existing problems in underground pipelines, such as light variations and small viewing angles. A lightweight approach for monitoring the locations of underground pipelines is achieved. The proposed approach’s effectiveness has been validated through laboratory simulation experiments, demonstrating accuracy at the millimeter level. |
| first_indexed | 2024-03-08T19:58:59Z |
| format | Article |
| id | doaj.art-3b0c3cbfdda44c5db06d862a149726bd |
| institution | Directory Open Access Journal |
| issn | 2467-9674 |
| language | English |
| last_indexed | 2024-03-08T19:58:59Z |
| publishDate | 2024-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Underground Space |
| spelling | doaj.art-3b0c3cbfdda44c5db06d862a149726bd2023-12-24T04:46:10ZengKeAi Communications Co., Ltd.Underground Space2467-96742024-04-01155975Location monitoring approach of underground pipelines using time-sequential imagesHaoruo Xu0Lei He1Yuyang Chu2Junchen He3Huaiguang Xiao4Chengmeng Shao5School of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, China; Corresponding author.School of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, Jiangsu, China; The 3rd branch, China Railway 16th Bureau Group Co., LTD, Huzhou 313000, Zhejiang, ChinaThe location monitoring of underground pipelines is of utmost significance as it helps the effective management and maintenance of the pipelines, and facilitates the planning of nearby projects, preventing damage to the pipelines. However, currently there is a serious lack of data on the locations of underground pipelines. This paper proposes an image-based approach for monitoring the locations of underground pipelines by combing deep learning and visual-based reconstruction. The proposed approach can build the monitoring model for underground pipelines and characterize their locations through their centroid curve. Its advantages are: (1) simplicity: it only requires time-sequential images of the inner walls of underground pipelines; (2) clarity: the location model and the location curve of underground pipelines can be provided quickly; (3) robustness: it can cope with some existing problems in underground pipelines, such as light variations and small viewing angles. A lightweight approach for monitoring the locations of underground pipelines is achieved. The proposed approach’s effectiveness has been validated through laboratory simulation experiments, demonstrating accuracy at the millimeter level.http://www.sciencedirect.com/science/article/pii/S2467967423001113Underground pipelinesLocation monitoringTime-sequential imagesVisual-based reconstructionDeep learning |
| spellingShingle | Haoruo Xu Lei He Yuyang Chu Junchen He Huaiguang Xiao Chengmeng Shao Location monitoring approach of underground pipelines using time-sequential images Underground Space Underground pipelines Location monitoring Time-sequential images Visual-based reconstruction Deep learning |
| title | Location monitoring approach of underground pipelines using time-sequential images |
| title_full | Location monitoring approach of underground pipelines using time-sequential images |
| title_fullStr | Location monitoring approach of underground pipelines using time-sequential images |
| title_full_unstemmed | Location monitoring approach of underground pipelines using time-sequential images |
| title_short | Location monitoring approach of underground pipelines using time-sequential images |
| title_sort | location monitoring approach of underground pipelines using time sequential images |
| topic | Underground pipelines Location monitoring Time-sequential images Visual-based reconstruction Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2467967423001113 |
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