Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge
For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/14/2665 |
| _version_ | 1827686911353815040 |
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| author | Ali Mirzazade Cosmin Popescu Thomas Blanksvärd Björn Täljsten |
| author_facet | Ali Mirzazade Cosmin Popescu Thomas Blanksvärd Björn Täljsten |
| author_sort | Ali Mirzazade |
| collection | DOAJ |
| description | For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential. |
| first_indexed | 2024-03-10T09:25:18Z |
| format | Article |
| id | doaj.art-16c7a85355314a28aa81a58603d90690 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:25:18Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-16c7a85355314a28aa81a58603d906902023-11-22T04:50:37ZengMDPI AGRemote Sensing2072-42922021-07-011314266510.3390/rs13142665Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk BridgeAli Mirzazade0Cosmin Popescu1Thomas Blanksvärd2Björn Täljsten3Structural and Fire Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, SwedenStructural and Fire Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, SwedenStructural and Fire Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, SwedenStructural and Fire Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, SwedenFor the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.https://www.mdpi.com/2072-4292/13/14/2665bridge inspectioncomputer visionintelligent hierarchical DSfMbridge 3D modelingdamage detectiondamage segmentation |
| spellingShingle | Ali Mirzazade Cosmin Popescu Thomas Blanksvärd Björn Täljsten Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge Remote Sensing bridge inspection computer vision intelligent hierarchical DSfM bridge 3D modeling damage detection damage segmentation |
| title | Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge |
| title_full | Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge |
| title_fullStr | Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge |
| title_full_unstemmed | Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge |
| title_short | Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge |
| title_sort | workflow for off site bridge inspection using automatic damage detection case study of the pahtajokk bridge |
| topic | bridge inspection computer vision intelligent hierarchical DSfM bridge 3D modeling damage detection damage segmentation |
| url | https://www.mdpi.com/2072-4292/13/14/2665 |
| work_keys_str_mv | AT alimirzazade workflowforoffsitebridgeinspectionusingautomaticdamagedetectioncasestudyofthepahtajokkbridge AT cosminpopescu workflowforoffsitebridgeinspectionusingautomaticdamagedetectioncasestudyofthepahtajokkbridge AT thomasblanksvard workflowforoffsitebridgeinspectionusingautomaticdamagedetectioncasestudyofthepahtajokkbridge AT bjorntaljsten workflowforoffsitebridgeinspectionusingautomaticdamagedetectioncasestudyofthepahtajokkbridge |