Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9913977/ |
| _version_ | 1828343019182489600 |
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| author | Mateusz Zarski Bartosz Wojcik Jaroslaw A. Miszczak Bartlomiej Blachowski Mariusz Ostrowski |
| author_facet | Mateusz Zarski Bartosz Wojcik Jaroslaw A. Miszczak Bartlomiej Blachowski Mariusz Ostrowski |
| author_sort | Mateusz Zarski |
| collection | DOAJ |
| description | The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight. |
| first_indexed | 2024-04-13T23:37:32Z |
| format | Article |
| id | doaj.art-d973c2f578814e88bec4a1f025d0768e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T23:37:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d973c2f578814e88bec4a1f025d0768e2022-12-22T02:24:41ZengIEEEIEEE Access2169-35362022-01-011010813410814410.1109/ACCESS.2022.32129189913977Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic DatasetMateusz Zarski0https://orcid.org/0000-0003-0380-8411Bartosz Wojcik1https://orcid.org/0000-0003-0695-8386Jaroslaw A. Miszczak2https://orcid.org/0000-0001-8790-101XBartlomiej Blachowski3https://orcid.org/0000-0001-6021-0374Mariusz Ostrowski4https://orcid.org/0000-0003-2388-5203Faculty of Civil Engineering, Silesian University of Technology, Gliwice, PolandFaculty of Civil Engineering, Silesian University of Technology, Gliwice, PolandInstitute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, PolandInstitute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, PolandInstitute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, PolandThe area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.https://ieeexplore.ieee.org/document/9913977/Structural health monitoringmachine learningdefect detectionsynthetic dataset |
| spellingShingle | Mateusz Zarski Bartosz Wojcik Jaroslaw A. Miszczak Bartlomiej Blachowski Mariusz Ostrowski Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset IEEE Access Structural health monitoring machine learning defect detection synthetic dataset |
| title | Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset |
| title_full | Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset |
| title_fullStr | Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset |
| title_full_unstemmed | Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset |
| title_short | Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset |
| title_sort | computer vision based inspection on post earthquake with uav synthetic dataset |
| topic | Structural health monitoring machine learning defect detection synthetic dataset |
| url | https://ieeexplore.ieee.org/document/9913977/ |
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