论文标题
迈向自动威胁检测：X射线安全成像中深度学习进步的调查
Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging
论文作者
论文摘要
X射线安全筛选被广泛用于维持航空/运输安全性，其意义对自动筛选系统产生了特别的兴趣。本文旨在通过将现场分类为传统的机器学习和当代深度学习应用来回顾计算机化的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古典机器学习方法，而后一部分则彻底研究了现代深度学习算法的使用。拟议的分类学将深度学习方法的使用量为监督，半监督和无监督的学习，并特别关注对象分类，检测，细分和异常检测任务。该论文进一步探讨了良好的X射线数据集并提供了性能基准。根据深度学习的当前和未来趋势，本文最终提出了X射线安全图像的讨论和未来方向。
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.
