Abstract
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.
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Acknowledgements
This research was supported by a grant (20CTAP-C151784-02) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government, and the Basic Science Research Program funded by the Ministry of Science, ICT, and Future Planning and the National Research Foundation of Korea (NRF-2016R1C1B2014997).
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Won, D., Chi, S. & Park, MW. UAV-RFID Integration for Construction Resource Localization. KSCE J Civ Eng 24, 1683–1695 (2020). https://doi.org/10.1007/s12205-020-2074-y
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DOI: https://doi.org/10.1007/s12205-020-2074-y