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

Akcay, S. and Breckon, T.P. (2022) 'Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging.', Pattern Recognition, 122 . p. 108245.

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.

Item Type:Article
Full text:Publisher-imposed embargo until 08 September 2022.
(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives 4.0.
File format - PDF
(2036Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.patcog.2021.108245
Publisher 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/
Date accepted:10 August 2021
Date deposited:24 August 2021
Date of first online publication:08 September 2021
Date first made open access:08 September 2022

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