  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.
