Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait
This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accident...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/9/442 |
| _version_ | 1797553757189308416 |
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| author | Abdullah M. Almeshal Mohammad R. Alenezi Abdullah K. Alshatti |
| author_facet | Abdullah M. Almeshal Mohammad R. Alenezi Abdullah K. Alshatti |
| author_sort | Abdullah M. Almeshal |
| collection | DOAJ |
| description | This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments. |
| first_indexed | 2024-03-10T16:21:08Z |
| format | Article |
| id | doaj.art-055ec42d2fc44d44a1479d218125f4ff |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T16:21:08Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-055ec42d2fc44d44a1479d218125f4ff2023-11-20T13:39:19ZengMDPI AGInformation2078-24892020-09-0111944210.3390/info11090442Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of KuwaitAbdullah M. Almeshal0Mohammad R. Alenezi1Abdullah K. Alshatti2Department of Electronic Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Safat 13092, KuwaitDepartment of Electronic Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training, Safat 13092, KuwaitAutomatic Control and Systems Engineering Department, University of Sheffield, Sheffield S10 2TN, UKThis study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments.https://www.mdpi.com/2078-2489/11/9/442UAVaerial robotic vehiclephotogrammetryrobustness |
| spellingShingle | Abdullah M. Almeshal Mohammad R. Alenezi Abdullah K. Alshatti Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait Information UAV aerial robotic vehicle photogrammetry robustness |
| title | Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait |
| title_full | Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait |
| title_fullStr | Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait |
| title_full_unstemmed | Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait |
| title_short | Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait |
| title_sort | accuracy assessment of small unmanned aerial vehicle for traffic accident photogrammetry in the extreme operating conditions of kuwait |
| topic | UAV aerial robotic vehicle photogrammetry robustness |
| url | https://www.mdpi.com/2078-2489/11/9/442 |
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