Department of Computer Science, Durham University, UK.
Information Technology Department, Faculty of Computers and Information, Menoufia University, Egypt.
J Xray Sci Technol. 2020;28(1):35-58. doi: 10.3233/XST-190531.
The screening of baggage using X-ray scanners is now routine in aviation security with automatic threat detection approaches, based on 3D X-ray computed tomography (CT) images, known as Automatic Threat Recognition (ATR) within the aviation security industry. These current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. To address this issue, the concept of adaptive automatic threat recognition (AATR) was proposed in previous work.
In this paper, we present a solution to AATR based on such X-ray CT baggage scan imagery. This aims to address the issues of rapidly evolving threat signatures within the screening requirements. Ideally, the detection algorithms deployed within the security scanners should be readily adaptable to different situations with varying requirements of threat characteristics (e.g., threat material, physical properties of objects).
We tackle this issue using a novel adaptive machine learning methodology with our solution consisting of a multi-scale 3D CT image segmentation algorithm, a multi-class support vector machine (SVM) classifier for object material recognition and a strategy to enable the adaptability of our approach. Experiments are conducted on both open and sequestered 3D CT baggage image datasets specifically collected for the AATR study.
Our proposed approach performs well on both recognition and adaptation. Overall our approach can achieve the probability of detection around 90% with a probability of false alarm below 20%.
Our AATR shows the capabilities of adapting to varying types of materials, even the unknown materials which are not available in the training data, adapting to varying required probability of detection and adapting to varying scales of the threat object.
在航空安全领域，利用 X 射线扫描仪对行李进行筛查已经成为常规操作，其中自动威胁检测方法基于三维 X 射线计算机断层扫描（CT）图像，在航空安全行业内被称为自动威胁识别（ATR）。这些当前策略使用预定义的威胁物质特征，与针对新出现的威胁特征的适应性形成对比。为了解决这个问题，在之前的工作中提出了自适应自动威胁识别（AATR）的概念。
在本文中，我们提出了一种基于这种 X 射线 CT 行李扫描图像的 AATR 解决方案。其旨在解决筛查要求中快速演变的威胁特征的问题。理想情况下，安全扫描仪中部署的检测算法应该能够轻松适应不同的情况，不同的情况可能具有不同的威胁特征要求（例如，威胁物质、物体的物理性质）。
我们使用一种新颖的自适应机器学习方法来解决这个问题，我们的解决方案包括一个多尺度 3D CT 图像分割算法、一个用于物体材料识别的多类支持向量机（SVM）分类器，以及一种使我们的方法具有适应性的策略。实验是在专门为 AATR 研究收集的公开和隔离的 3D CT 行李图像数据集上进行的。
我们提出的方法在识别和适应方面都表现良好。总体而言，我们的方法可以实现约 90%的检测概率和低于 20%的误报率。
我们的 AATR 展示了适应不同类型材料的能力，甚至可以适应训练数据中不存在的未知材料，适应不同的检测概率要求，以及适应不同规模的威胁物体。
