Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach

Seungeon Song; Donghoon Kwak; Youngduk Kim; Jonghun Lee

doi:10.1109/IRMMW-THz57677.2023.10299311

Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach

Seungeon Song
, Donghoon Kwak
, Youngduk Kim
, Jonghun Lee

Division of Automotive Technology

Daegu Gyeongbuk Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350336603
DOIs	https://doi.org/10.1109/IRMMW-THz57677.2023.10299311
State	Published - 2023
Event	48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023 - Montreal, Canada Duration: 17 Sep 2023 → 22 Sep 2023

Publication series

Name	International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz
ISSN (Print)	2162-2027
ISSN (Electronic)	2162-2035

Conference

Conference	48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023
Country/Territory	Canada
City	Montreal
Period	17/09/23 → 22/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/IRMMW-THz57677.2023.10299311

Cite this

Song, S., Kwak, D., Kim, Y., & Lee, J. (2023). Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach. In IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves (International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz). IEEE Computer Society. https://doi.org/10.1109/IRMMW-THz57677.2023.10299311

Song, Seungeon ; Kwak, Donghoon ; Kim, Youngduk et al. / Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products : An Automatic Inspection Approach. IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves. IEEE Computer Society, 2023. (International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz).

@inproceedings{48bc3d660e7b4752a8822203aa8b5309,

title = "Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach",

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.",

author = "Seungeon Song and Donghoon Kwak and Youngduk Kim and Jonghun Lee",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023 ; Conference date: 17-09-2023 Through 22-09-2023",

year = "2023",

doi = "10.1109/IRMMW-THz57677.2023.10299311",

language = "English",

series = "International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz",

publisher = "IEEE Computer Society",

booktitle = "IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves",

}

Song, S, Kwak, D, Kim, Y & Lee, J 2023, Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach. in IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves. International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz, IEEE Computer Society, 48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023, Montreal, Canada, 17/09/23. https://doi.org/10.1109/IRMMW-THz57677.2023.10299311

Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach. / Song, Seungeon; Kwak, Donghoon; Kim, Youngduk et al.
IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves. IEEE Computer Society, 2023. (International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products

T2 - 48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023

AU - Song, Seungeon

AU - Kwak, Donghoon

AU - Kim, Youngduk

AU - Lee, Jonghun

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85177681594

U2 - 10.1109/IRMMW-THz57677.2023.10299311

DO - 10.1109/IRMMW-THz57677.2023.10299311

M3 - Conference contribution

AN - SCOPUS:85177681594

T3 - International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz

BT - IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves

PB - IEEE Computer Society

Y2 - 17 September 2023 through 22 September 2023

ER -

Song S, Kwak D, Kim Y , Lee J. Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach. In IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves. IEEE Computer Society. 2023. (International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz). doi: 10.1109/IRMMW-THz57677.2023.10299311

Terahertz Radar and Deep Learning-Based Detection of Soft Foreign Objects in Food Products: An Automatic Inspection Approach

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