Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete
In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/1837800 |
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| _version_ | 1832549741120978944 |
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| author | Xiaoming You Gongxing Yan Zhengqiang Yang |
| author_facet | Xiaoming You Gongxing Yan Zhengqiang Yang |
| author_sort | Xiaoming You |
| collection | DOAJ |
| description | In recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation. |
| format | Article |
| id | doaj-art-b191f6c77b794de7856882baa1ede4a8 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-b191f6c77b794de7856882baa1ede4a82025-02-03T06:08:43ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/1837800Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining ConcreteXiaoming You0Gongxing Yan1Zhengqiang Yang2Chongqing Vocational Institute of EngineeringCollege of Architectural EngineeringJiangsu Vocational Institute of Architectural TechnologyIn recent years, with the rapid development of tunnel construction in China, the length of tunnels has continued to increase, and the consequent tunnel disease detection has attracted more and more attention from maintenance departments. Among many diseases, lining cracks are the most common, which directly reflect the stress of the lining, which is very important for the study of tunnel diseases. In view of the current detection status and detection requirements, this article has carried out research work on a vehicle-mounted tunnel lining crack detection system based on image processing. Due to the grayscale difference between the cracks on the lining surface and the lining background, these differences lead to significant crack edge features and relatively stable detection. Therefore, this article designs an intelligent edge algorithm system for cracks on the lining surface to detect the edges of the image, extract the edges of cracks, and remove useless interference information in the lining background. The experiment proves that the paired sample t-test can find that after the experiment is over, the P value of different edge detection operators for global threshold segmentation is less than 0.05, which has a significant difference. The Canny, Deriche, and Lanser filters are relatively strong, and the extracted crack edge noise is relatively small. Finally, the parameter values of the crack image are calculated, and the calculated values of the crack parameters provide a scientific and reliable basis for tunnel safety evaluation.http://dx.doi.org/10.1155/2022/1837800 |
| spellingShingle | Xiaoming You Gongxing Yan Zhengqiang Yang Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete International Transactions on Electrical Energy Systems |
| title | Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete |
| title_full | Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete |
| title_fullStr | Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete |
| title_full_unstemmed | Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete |
| title_short | Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete |
| title_sort | intelligent edge computing detection vehicle and detection method based on tunnel lining concrete |
| url | http://dx.doi.org/10.1155/2022/1837800 |
| work_keys_str_mv | AT xiaomingyou intelligentedgecomputingdetectionvehicleanddetectionmethodbasedontunnelliningconcrete AT gongxingyan intelligentedgecomputingdetectionvehicleanddetectionmethodbasedontunnelliningconcrete AT zhengqiangyang intelligentedgecomputingdetectionvehicleanddetectionmethodbasedontunnelliningconcrete |