Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation
Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are co...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/16/7159 |
| _version_ | 1797583270416744448 |
|---|---|
| author | Siyuan Chen Xiangding Zeng Debra F. Laefer Linh Truong-Hong Eleni Mangina |
| author_facet | Siyuan Chen Xiangding Zeng Debra F. Laefer Linh Truong-Hong Eleni Mangina |
| author_sort | Siyuan Chen |
| collection | DOAJ |
| description | Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection. |
| first_indexed | 2024-03-10T23:35:32Z |
| format | Article |
| id | doaj.art-4805f7617d624ce1a73aa3d39535a45a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:35:32Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4805f7617d624ce1a73aa3d39535a45a2023-11-19T02:57:39ZengMDPI AGSensors1424-82202023-08-012316715910.3390/s23167159Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck DocumentationSiyuan Chen0Xiangding Zeng1Debra F. Laefer2Linh Truong-Hong3Eleni Mangina4School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414015, ChinaCollege of Mechanical Engineering, Hunan Institute of Science and Technology, Yueyang 414015, ChinaSchool of Civil Engineering, University College Dublin, D04C1P1 Dublin, IrelandSchool of Civil Engineering, Technical University Delft, 2628 CD Delft, The NetherlandsSchool of Computer Science, University College Dublin, D04C1P1 Dublin, IrelandImagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.https://www.mdpi.com/1424-8220/23/16/7159UAVSFMphotogrammetrypoint cloudquality evaluation |
| spellingShingle | Siyuan Chen Xiangding Zeng Debra F. Laefer Linh Truong-Hong Eleni Mangina Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation Sensors UAV SFM photogrammetry point cloud quality evaluation |
| title | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
| title_full | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
| title_fullStr | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
| title_full_unstemmed | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
| title_short | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
| title_sort | flight path setting and data quality assessments for unmanned aerial vehicle based photogrammetric bridge deck documentation |
| topic | UAV SFM photogrammetry point cloud quality evaluation |
| url | https://www.mdpi.com/1424-8220/23/16/7159 |
| work_keys_str_mv | AT siyuanchen flightpathsettinganddataqualityassessmentsforunmannedaerialvehiclebasedphotogrammetricbridgedeckdocumentation AT xiangdingzeng flightpathsettinganddataqualityassessmentsforunmannedaerialvehiclebasedphotogrammetricbridgedeckdocumentation AT debraflaefer flightpathsettinganddataqualityassessmentsforunmannedaerialvehiclebasedphotogrammetricbridgedeckdocumentation AT linhtruonghong flightpathsettinganddataqualityassessmentsforunmannedaerialvehiclebasedphotogrammetricbridgedeckdocumentation AT elenimangina flightpathsettinganddataqualityassessmentsforunmannedaerialvehiclebasedphotogrammetricbridgedeckdocumentation |