Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing
In Japan, all bridges should be inspected every 5 years. Usually, the inspection has been performed through the visual evaluation of experienced engineers. However, it requires a lot of load and expense. In order to reduce the inspection work, an attempt is made in this paper to develop a new inspec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Built Environment |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2022.972796/full |
| _version_ | 1798000236143050752 |
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| author | Yasutoshi Nomura Masaya Inoue Hitoshi Furuta |
| author_facet | Yasutoshi Nomura Masaya Inoue Hitoshi Furuta |
| author_sort | Yasutoshi Nomura |
| collection | DOAJ |
| description | In Japan, all bridges should be inspected every 5 years. Usually, the inspection has been performed through the visual evaluation of experienced engineers. However, it requires a lot of load and expense. In order to reduce the inspection work, an attempt is made in this paper to develop a new inspection method using deep learning and image processing technologies. While using the photos obtained by vehicle-mounted camera, the damage states of bridges can be evaluated manually, it still requires a lot of time and load. To save the time and load, deep learning, which is a method of artificial intelligence is introduced. For image processing, it is necessary to utilize such pre-processing techniques as binarization of pictures and morphology treatment. To illustrate the applicability of the method developed here, some experiments are conducted by using the photos of running surface of concrete bridges of a monorail took by vehicle-mounted camera. |
| first_indexed | 2024-04-11T11:17:16Z |
| format | Article |
| id | doaj.art-2afa4976cd7c4237a8e1f4a678ea6177 |
| institution | Directory Open Access Journal |
| issn | 2297-3362 |
| language | English |
| last_indexed | 2024-04-11T11:17:16Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Built Environment |
| spelling | doaj.art-2afa4976cd7c4237a8e1f4a678ea61772022-12-22T04:27:11ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622022-09-01810.3389/fbuil.2022.972796972796Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processingYasutoshi Nomura0Masaya Inoue1Hitoshi Furuta2Department of Civil and Environmental Engineering, Ritsumeikan University, Kusatsu, JapanDepartment of Civil and Environmental Engineering, Ritsumeikan University, Kusatsu, JapanDepartment of Civil Engineering, Osaka Metropolitan University, Osaka, JapanIn Japan, all bridges should be inspected every 5 years. Usually, the inspection has been performed through the visual evaluation of experienced engineers. However, it requires a lot of load and expense. In order to reduce the inspection work, an attempt is made in this paper to develop a new inspection method using deep learning and image processing technologies. While using the photos obtained by vehicle-mounted camera, the damage states of bridges can be evaluated manually, it still requires a lot of time and load. To save the time and load, deep learning, which is a method of artificial intelligence is introduced. For image processing, it is necessary to utilize such pre-processing techniques as binarization of pictures and morphology treatment. To illustrate the applicability of the method developed here, some experiments are conducted by using the photos of running surface of concrete bridges of a monorail took by vehicle-mounted camera.https://www.frontiersin.org/articles/10.3389/fbuil.2022.972796/fullcrack detectiondeep learningcrack propagationconcrete bridgeimage processing |
| spellingShingle | Yasutoshi Nomura Masaya Inoue Hitoshi Furuta Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing Frontiers in Built Environment crack detection deep learning crack propagation concrete bridge image processing |
| title | Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing |
| title_full | Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing |
| title_fullStr | Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing |
| title_full_unstemmed | Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing |
| title_short | Evaluation of crack propagation in concrete bridges from vehicle-mounted camera images using deep learning and image processing |
| title_sort | evaluation of crack propagation in concrete bridges from vehicle mounted camera images using deep learning and image processing |
| topic | crack detection deep learning crack propagation concrete bridge image processing |
| url | https://www.frontiersin.org/articles/10.3389/fbuil.2022.972796/full |
| work_keys_str_mv | AT yasutoshinomura evaluationofcrackpropagationinconcretebridgesfromvehiclemountedcameraimagesusingdeeplearningandimageprocessing AT masayainoue evaluationofcrackpropagationinconcretebridgesfromvehiclemountedcameraimagesusingdeeplearningandimageprocessing AT hitoshifuruta evaluationofcrackpropagationinconcretebridgesfromvehiclemountedcameraimagesusingdeeplearningandimageprocessing |