Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
UAV-RFID Integration for Construction Resource Localization
https://orcid.org/http://orcid.org/0000-0002-0409-5268
https://orcid.org/http://orcid.org/0000-0002-3844-9327
Location data of construction resources are important in understanding on the context of a construction site, yet most sites still rely on people’s observations to localize their resources. Among then various localization technologies, radio frequency identification (RFID) is considered as a good solution. However, RFID either provides limited location data when fixed receivers are used, or it requires considerable manpower for scanning the tagged resources when hand-held receivers are used. These requirements result in inefficiency and impractical demands on time and cost, particularly in the case of complex or large-scale sites. This study attempted to overcome the limitations by proposing an integrated unmanned aerial vehicle-RFID (UAV-RFID) platform to replace the considerable manpower with the UAV and to enable identifying tags on a site. It applies deep learning algorithms to localize an RFID tag position within an acceptable range of accuracy, thereby demonstrating the feasibility of the integrated platform for construction resource localization.
UAV-RFID Integration for Construction Resource Localization
https://orcid.org/http://orcid.org/0000-0002-0409-5268
https://orcid.org/http://orcid.org/0000-0002-3844-9327
Location data of construction resources are important in understanding on the context of a construction site, yet most sites still rely on people’s observations to localize their resources. Among then various localization technologies, radio frequency identification (RFID) is considered as a good solution. However, RFID either provides limited location data when fixed receivers are used, or it requires considerable manpower for scanning the tagged resources when hand-held receivers are used. These requirements result in inefficiency and impractical demands on time and cost, particularly in the case of complex or large-scale sites. This study attempted to overcome the limitations by proposing an integrated unmanned aerial vehicle-RFID (UAV-RFID) platform to replace the considerable manpower with the UAV and to enable identifying tags on a site. It applies deep learning algorithms to localize an RFID tag position within an acceptable range of accuracy, thereby demonstrating the feasibility of the integrated platform for construction resource localization.
UAV-RFID Integration for Construction Resource Localization
https://orcid.org/http://orcid.org/0000-0002-0409-5268
https://orcid.org/http://orcid.org/0000-0002-3844-9327
Location data of construction resources are important in understanding on the context of a construction site, yet most sites still rely on people’s observations to localize their resources. Among then various localization technologies, radio frequency identification (RFID) is considered as a good solution. However, RFID either provides limited location data when fixed receivers are used, or it requires considerable manpower for scanning the tagged resources when hand-held receivers are used. These requirements result in inefficiency and impractical demands on time and cost, particularly in the case of complex or large-scale sites. This study attempted to overcome the limitations by proposing an integrated unmanned aerial vehicle-RFID (UAV-RFID) platform to replace the considerable manpower with the UAV and to enable identifying tags on a site. It applies deep learning algorithms to localize an RFID tag position within an acceptable range of accuracy, thereby demonstrating the feasibility of the integrated platform for construction resource localization.
UAV-RFID Integration for Construction Resource Localization
KSCE J Civ Eng
Won, Daeyoun (Autor:in) / Chi, Seokho (Autor:in) / Park, Man-Woo (Autor:in)
KSCE Journal of Civil Engineering ; 24 ; 1683-1695
01.06.2020
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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