Abstract
This study evaluates the scalability of using sub-terahertz (THz) radar-based deep-learning techniques for automatically detecting soft foreign objects in dry foods. Previous research [1] has demonstrated that soft foreign objects can be detected with over 99% accuracy using deep learning models such as ResNet50-Fast R-CNN, combined with preprocessed transmission images. In this paper, we aim to assess the applicability to various food groups and packaging materials by constructing and analyzing a database of images acquired through sub-THz radar and area scanners.
| Original language | English |
|---|---|
| Title of host publication | 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2024 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9798350370324 |
| DOIs | |
| State | Published - 2024 |
| Event | 49th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2024 - Perth, Australia Duration: 1 Sep 2024 → 6 Sep 2024 |
Publication series
| Name | International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz |
|---|---|
| ISSN (Print) | 2162-2027 |
| ISSN (Electronic) | 2162-2035 |
Conference
| Conference | 49th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2024 |
|---|---|
| Country/Territory | Australia |
| City | Perth |
| Period | 1/09/24 → 6/09/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
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