Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging. (February 2022) - Akcay, Samet; Breckon, Toby
Highlights: Toxonomy - an extensive overview of classical machine learning and contemporary deep learning within X-ray security imaging. Datasets - an overview of the large datasets used to train deep learning approaches within the field. Open problems - discussion of the open problems, current challenges, and future directions based on the current trends within computer vision. 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 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.