Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Song, Seungeon | - |
dc.contributor.author | Kwak, Donghoon | - |
dc.contributor.author | Kim, Youngduk | - |
dc.contributor.author | Lee, Jonghun | - |
dc.date.accessioned | 2024-01-22T15:10:10Z | - |
dc.date.available | 2024-01-22T15:10:10Z | - |
dc.date.created | 2023-12-11 | - |
dc.date.issued | 2023-09-18 | - |
dc.identifier.isbn | 9798350336603 | - |
dc.identifier.issn | 2162-2035 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/47638 | - |
dc.description.abstract | This study investigates the feasibility of an automatic detection system for foreign objects in food using terahertz radar and deep learning techniques. The experimental setup comprises a terahertz radar source, beam splitters and collimators, and a terahertz receiver with a 32 x 32, 1024-pixel area scanner. Received images representing signal strength transmitted through noodles serve as input for a deep learning model after preprocessing to eliminate noise. A binary decision is then made on whether the food contains foreign objects or not. © 2023 IEEE. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1109/IRMMW-THz57677.2023.10299311 | - |
dc.identifier.scopusid | 2-s2.0-85177681594 | - |
dc.identifier.bibliographicCitation | 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz 2023), pp.1 - 2 | - |
dc.identifier.url | https://www.irmmw-thz.org/wp-content/uploads/2023/08/IRMMW-THz-2023-Conference.pdf | - |
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Montreal | - |
dc.citation.endPage | 2 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | 48th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz 2023) | - |
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