Few-shot concealed object detection in sub-THz security images using improved pseudo-annotations
File(s)
Published version
Supporting information
Author(s)
Cheng, Ran
Lucyszyn, Stepan
Type
Journal Article
Abstract
In this research, we explore the few-shot object detection application for identifying concealed objects in sub-terahertz security images, using fine-tuning based frameworks. To adapt these machine learning frameworks for the (sub-)terahertz domain, we propose an innovative pseudo-annotation method to augment the object detector by sourcing high-quality training samples from unlabeled images. This approach employs multiple one-class detectors coupled with a fine-grained classifier, trained on supporting thermal-infrared images, to prevent overfitting. Consequently, our approach enhances the model’s ability to detect challenging objects (e.g., 3D-printed guns and ceramic knives) when few-shot training examples are available, especially in the real-world scenario where images of concealed dangerous items are scarce.
Date Issued
2024-02-07
Date Acceptance
2024-01-27
Citation
Scientific Reports, 2024, 14, pp.1-9
ISSN
2045-2322
Publisher
Nature Portfolio
Start Page
1
End Page
9
Journal / Book Title
Scientific Reports
Volume
14
Copyright Statement
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.nature.com/articles/s41598-024-53045-9
Publication Status
Published
Article Number
3150
Date Publish Online
2024-02-07