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

• URL: http://arxiv.org/abs/2001.01293v2
• Date: Mon, 13 Sep 2021 05:30:05 GMT
• Title: Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
• Authors: Samet Akcay and Toby Breckon
• Abstract summary: 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 proposed taxonomy sub-categorises the use of deep learning approaches into supervised, semi-supervised and unsupervised learning. Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery.
• Score: 0.6091702876917279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 well-established 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.

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