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Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging

Akcay, S.; Breckon, T.P.

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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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