BACKGROUND 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. OBJECTIVE 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). METHODS 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. RESULTS 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%. CONCLUSIONS 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.

中文翻译：

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一种用于行李安全检查的3D计算机断层图像中的自适应自动威胁识别的方法。

背景技术现在，基于3D X射线计算机断层扫描（CT）图像，在航空安全行业中，使用X射线扫描仪对行李进行自动检测是基于自动威胁检测方法在航空安全中的常规做法。与针对新出现的威胁签名的适应性相反，这些当前策略使用预定义的威胁材料签名。为了解决这个问题，以前的工作中提出了自适应自动威胁识别（AATR）的概念。目的在本文中，我们提出了一种基于X射线CT行李扫描图像的AATR解决方案。这旨在解决筛查要求内迅速演变的威胁特征的问题。理想情况下，安全扫描器中部署的检测算法应易于适应具有不同威胁特征（例如威胁材料，物体的物理属性）要求的不同情况。方法我们使用一种新颖的自适应机器学习方法来解决此问题，其解决方案包括多尺度3D CT图像分割算法，用于物体材料识别的多类支持向量机（SVM）分类器以及使物体适应性提高的策略。我们的方法。针对专门为AATR研究而收集的开放式和隔离式3D CT行李图像数据集进行了实验。结果我们提出的方法在识别和适应方面都表现良好。总的来说，我们的方法可以实现90％左右的检测概率，而误报的可能性低于20％。