Dr Samet Akcay samet.akcay@durham.ac.uk
Academic Visitor
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
Akcay, S.; Breckon, T.P.
Authors
Professor Toby Breckon toby.breckon@durham.ac.uk
Professor
Abstract
X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications. The first part briefly discusses the classical machine learning approaches utilised within X-ray security imaging, while the latter part thoroughly investigates the use of modern deep learning algorithms. The proposed taxonomy sub-categorises the use of deep learning approaches into supervised, semi-supervised and unsupervised learning, with a particular focus on object classification, detection, segmentation and anomaly detection tasks. The paper further explores wellestablished X-ray datasets and provides a performance benchmark. Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery.
Citation
Akcay, S., & Breckon, T. (2022). Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging. Pattern Recognition, 122, Article 108245. https://doi.org/10.1016/j.patcog.2021.108245
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 10, 2021 |
Online Publication Date | Sep 8, 2021 |
Publication Date | 2022-02 |
Deposit Date | Aug 23, 2021 |
Publicly Available Date | Sep 8, 2022 |
Journal | Pattern Recognition |
Print ISSN | 0031-3203 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 122 |
Article Number | 108245 |
DOI | https://doi.org/10.1016/j.patcog.2021.108245 |
Public URL | https://durham-repository.worktribe.com/output/1266240 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
© 2021 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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