Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging
Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging
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DOI：
10.1016/j.patcog.2021.108245
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发表时间：
2021-09-20
影响因子：
8
通讯作者：
Breckon, Toby
中科院分区：
文献类型：
--
作者：
Akcay, Samet;Breckon, Toby
X射线安全检查被广泛用于维护航空/运输安全，其重要性引起了人们对自动检查系统的特别关注。本文旨在通过将该领域分类为传统的机器学习和当代的深度学习应用，来回顾计算机化X射线安全成像算法。第一部分简要讨论了X射线安全成像中使用的经典机器学习方法，而第二部分则深入研究了现代深度学习算法的使用。拟议的分类法将深度学习方法的使用细分为有监督和无监督学习，特别关注对象分类、检测、分割和异常检测任务。本文进一步探讨了完善的X射线数据集，并提供了一个性能基准。基于深度学习的当前和未来趋势，本文最后提出了X射线安全成像的讨论和未来方向。(c)2021由Elsevier Ltd.出版
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 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. (c) 2021 Published by Elsevier Ltd.