REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING
Facilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by bloc...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/43/2020/isprs-archives-XLIII-B2-2020-43-2020.pdf |
| _version_ | 1819262835216089088 |
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| author | H. Kim H. Kim S. Ham I. Lee |
| author_facet | H. Kim H. Kim S. Ham I. Lee |
| author_sort | H. Kim |
| collection | DOAJ |
| description | Facilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by blocking the facility or performing it late at night. In order to increase the efficiency of monitoring, research using images, especially drone images, was conducted. However, when using a drone image, there is a trade-off relationship between accuracy and processing time. In this study, we propose a real-time drone mapping based on reference images for efficient vehicle facility monitoring. The real-time drone mapping based on the reference image is composed of reference images build, aerial triangulation (AT) based on reference images (refAT), and orthophoto generation. The refAT refers to a method of performing AT by using a reference images as reference data. We compared the processing time and processing accuracy of direct georeferencing and refAT. We built 154 drone reference images in the target area. The refAT showed a processing time of about 8.95 seconds and an accuracy of 3.4 cm, and the direct georeferencing method showed a processing time of about 1.49 seconds and an accuracy of 22.5 m. If the method of this study is used for facility monitoring, it is expected that the efficiency of monitoring will be improved with speed and accuracy. |
| first_indexed | 2024-12-23T20:04:00Z |
| format | Article |
| id | doaj.art-17e76756736340e8bf67c213c05aa886 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-23T20:04:00Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-17e76756736340e8bf67c213c05aa8862022-12-21T17:32:59ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B2-2020434810.5194/isprs-archives-XLIII-B2-2020-43-2020REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORINGH. Kim0H. Kim1S. Ham2I. Lee3Lab for Sensor and Modeling, Department of Geoinformatics, University of Seoul, 163, Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of KoreaInnoPAM, 77, Cheongpa-ro, Yongsan-gu, Seoul, Republic of KoreaLab for Sensor and Modeling, Department of Geoinformatics, University of Seoul, 163, Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of KoreaLab for Sensor and Modeling, Department of Geoinformatics, University of Seoul, 163, Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of KoreaFacilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by blocking the facility or performing it late at night. In order to increase the efficiency of monitoring, research using images, especially drone images, was conducted. However, when using a drone image, there is a trade-off relationship between accuracy and processing time. In this study, we propose a real-time drone mapping based on reference images for efficient vehicle facility monitoring. The real-time drone mapping based on the reference image is composed of reference images build, aerial triangulation (AT) based on reference images (refAT), and orthophoto generation. The refAT refers to a method of performing AT by using a reference images as reference data. We compared the processing time and processing accuracy of direct georeferencing and refAT. We built 154 drone reference images in the target area. The refAT showed a processing time of about 8.95 seconds and an accuracy of 3.4 cm, and the direct georeferencing method showed a processing time of about 1.49 seconds and an accuracy of 22.5 m. If the method of this study is used for facility monitoring, it is expected that the efficiency of monitoring will be improved with speed and accuracy.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/43/2020/isprs-archives-XLIII-B2-2020-43-2020.pdf |
| spellingShingle | H. Kim H. Kim S. Ham I. Lee REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING |
| title_full | REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING |
| title_fullStr | REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING |
| title_full_unstemmed | REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING |
| title_short | REAL-TIME DRONE MAPPING BASED ON REFERENCE IMAGES FOR VEHICLE FACILITY MONITORING |
| title_sort | real time drone mapping based on reference images for vehicle facility monitoring |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/43/2020/isprs-archives-XLIII-B2-2020-43-2020.pdf |
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