A point cloud segmentation and material statistics algorithm for train carriage
The efficiency of train transportation in the port environment directly restricts the production efficiency of the entire port. In order to coordinate the productivity of the entire port and further improve the loading and unloading efficiency of the train automation system, this paper proposes a ma...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-03-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940221092043 |
| _version_ | 1797859146583769088 |
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| author | Xiaoshu Qiao Shan Gao Weichong Cao Chao Wang Jun Liu Kai Zhou |
| author_facet | Xiaoshu Qiao Shan Gao Weichong Cao Chao Wang Jun Liu Kai Zhou |
| author_sort | Xiaoshu Qiao |
| collection | DOAJ |
| description | The efficiency of train transportation in the port environment directly restricts the production efficiency of the entire port. In order to coordinate the productivity of the entire port and further improve the loading and unloading efficiency of the train automation system, this paper proposes a material segmentation and data statistics algorithm for the problem of material identification in the train compartment. The terrestrial laser scanning system was established under the experimental scenario of CHN ENERGY Tianjin Port. By fusing the laser point cloud data, and then performing pre-processing operations such as box filtering, down-sampling, and radius filtering, the pre-processed point cloud data is projected onto a two-dimensional plane as a two-dimensional image, and the canny operator is used to extract the contour of the carriage on the image. Further fitting to the plane of the point cloud of the carriage wall, and the point cloud of materials in the carriage is segmented. Through the method of slicing the compartment, completing the statistical analysis of the data in the compartment from the whole to the part. The database fields of the main carriage types are established to realize the information interaction between the database and the recognition algorithm. The final experimental results show that the error rate of the carriage length recognition is 5.89%, and the width error rate is 7.25%, which verifies that the algorithm has good recognition accuracy which fully meets engineering needs. |
| first_indexed | 2024-04-09T21:25:49Z |
| format | Article |
| id | doaj.art-5467a0326237461d9726a42b53169dde |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-04-09T21:25:49Z |
| publishDate | 2023-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-5467a0326237461d9726a42b53169dde2023-03-27T15:33:31ZengSAGE PublishingMeasurement + Control0020-29402023-03-015610.1177/00202940221092043A point cloud segmentation and material statistics algorithm for train carriageXiaoshu QiaoShan GaoWeichong CaoChao WangJun LiuKai ZhouThe efficiency of train transportation in the port environment directly restricts the production efficiency of the entire port. In order to coordinate the productivity of the entire port and further improve the loading and unloading efficiency of the train automation system, this paper proposes a material segmentation and data statistics algorithm for the problem of material identification in the train compartment. The terrestrial laser scanning system was established under the experimental scenario of CHN ENERGY Tianjin Port. By fusing the laser point cloud data, and then performing pre-processing operations such as box filtering, down-sampling, and radius filtering, the pre-processed point cloud data is projected onto a two-dimensional plane as a two-dimensional image, and the canny operator is used to extract the contour of the carriage on the image. Further fitting to the plane of the point cloud of the carriage wall, and the point cloud of materials in the carriage is segmented. Through the method of slicing the compartment, completing the statistical analysis of the data in the compartment from the whole to the part. The database fields of the main carriage types are established to realize the information interaction between the database and the recognition algorithm. The final experimental results show that the error rate of the carriage length recognition is 5.89%, and the width error rate is 7.25%, which verifies that the algorithm has good recognition accuracy which fully meets engineering needs.https://doi.org/10.1177/00202940221092043 |
| spellingShingle | Xiaoshu Qiao Shan Gao Weichong Cao Chao Wang Jun Liu Kai Zhou A point cloud segmentation and material statistics algorithm for train carriage Measurement + Control |
| title | A point cloud segmentation and material statistics algorithm for train carriage |
| title_full | A point cloud segmentation and material statistics algorithm for train carriage |
| title_fullStr | A point cloud segmentation and material statistics algorithm for train carriage |
| title_full_unstemmed | A point cloud segmentation and material statistics algorithm for train carriage |
| title_short | A point cloud segmentation and material statistics algorithm for train carriage |
| title_sort | point cloud segmentation and material statistics algorithm for train carriage |
| url | https://doi.org/10.1177/00202940221092043 |
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