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

Akcay, S.; Breckon, T.P.

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