Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry
Oscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/8/2223 |
| _version_ | 1797570594582036480 |
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| author | Omar El-Kadi Adel El-Shazly Khaled Nassar |
| author_facet | Omar El-Kadi Adel El-Shazly Khaled Nassar |
| author_sort | Omar El-Kadi |
| collection | DOAJ |
| description | Oscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by terrestrial photogrammetry using non-metric cameras. Tedious camera calibration procedures are eliminated by using a grid target that allows geometric correction to be performed to the frame’s region of interest at which oscillations are monitored. Region-based convolutional neural networks (Faster R-CNN) techniques are adopted to minimize the light exposure limitations, commonly constraining applications of terrestrial photogrammetry. The proposed monitoring procedure is tested at outdoor conditions to check its reliability and accuracy and examining the effect of using Faster R-CNN on monitoring results. The proposed artificial intelligence (AI) aided oscillation monitoring allowed sub-millimeter accuracy monitoring with observation rates up to 60 frames per second and gained the benefit of high optical zoom offered by market available bridge cameras to monitor oscillation of targets 100 m apart with high accuracy. |
| first_indexed | 2024-03-10T20:27:44Z |
| format | Article |
| id | doaj.art-0c2d02e8a4864fe9999a33766ac6f65d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:27:44Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0c2d02e8a4864fe9999a33766ac6f65d2023-11-19T21:39:59ZengMDPI AGSensors1424-82202020-04-01208222310.3390/s20082223Robust In-Plane Structures Oscillation Monitoring by Terrestrial PhotogrammetryOmar El-Kadi0Adel El-Shazly1Khaled Nassar2Civil Engineering, Faculty of Engineering, Cairo University, Giza Governorate 12613, EgyptCivil Engineering, Faculty of Engineering, Cairo University, Giza Governorate 12613, EgyptConstruction Engineering Department, The American University in Cairo, Cairo 11865, EgyptOscillation monitoring commonly requires complex setups integrating various types of sensors associated with intensive computations to achieve an adequate rate of observations and accuracy. This research presents a simple, cost-effective approach that allows two-dimensional oscillation monitoring by terrestrial photogrammetry using non-metric cameras. Tedious camera calibration procedures are eliminated by using a grid target that allows geometric correction to be performed to the frame’s region of interest at which oscillations are monitored. Region-based convolutional neural networks (Faster R-CNN) techniques are adopted to minimize the light exposure limitations, commonly constraining applications of terrestrial photogrammetry. The proposed monitoring procedure is tested at outdoor conditions to check its reliability and accuracy and examining the effect of using Faster R-CNN on monitoring results. The proposed artificial intelligence (AI) aided oscillation monitoring allowed sub-millimeter accuracy monitoring with observation rates up to 60 frames per second and gained the benefit of high optical zoom offered by market available bridge cameras to monitor oscillation of targets 100 m apart with high accuracy.https://www.mdpi.com/1424-8220/20/8/2223automationdeep learningdeformation monitoringFaster R-CNNimage processingoscillation monitoring |
| spellingShingle | Omar El-Kadi Adel El-Shazly Khaled Nassar Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry Sensors automation deep learning deformation monitoring Faster R-CNN image processing oscillation monitoring |
| title | Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry |
| title_full | Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry |
| title_fullStr | Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry |
| title_full_unstemmed | Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry |
| title_short | Robust In-Plane Structures Oscillation Monitoring by Terrestrial Photogrammetry |
| title_sort | robust in plane structures oscillation monitoring by terrestrial photogrammetry |
| topic | automation deep learning deformation monitoring Faster R-CNN image processing oscillation monitoring |
| url | https://www.mdpi.com/1424-8220/20/8/2223 |
| work_keys_str_mv | AT omarelkadi robustinplanestructuresoscillationmonitoringbyterrestrialphotogrammetry AT adelelshazly robustinplanestructuresoscillationmonitoringbyterrestrialphotogrammetry AT khalednassar robustinplanestructuresoscillationmonitoringbyterrestrialphotogrammetry |