箱包三维图像中电子器件的检测
Detecting Electric Devices in 3D Images of Bags
摘要：
航空和运输安全行业面临的挑战是在尽可能短的时间内检查大量行李中的威胁和违禁品。此过程的自动化和半自动化可以通过检测更多威胁来提高安全性，并通过加快流程来改善客户体验。传统的2D X射线图像通常非常难以检查，因为它们是紧密包装的，并且包含各种各样的杂乱和遮挡的对象。由于这些限制，主要机场正在引入3D X射线计算机断层扫描（CT）行李扫描。我们调查我们是否可以自动化的过程中检测电子设备在这些三维图像的行李。检测电气装置特别令人关注，因为它们可用于隐藏爆炸物。考虑到需要对这种威胁进行筛查的大量行李，自动检测的最佳方法是首先过滤行李中是否包含电子设备，如果包含，则识别设备的数量及其位置。我们提出了一种算法，Unpack，Predict，eXtract，Repack（UXPR），它涉及通过使用称为Sieve的算法在一系列尺度上分割数据来解包，基于体素强度的直方图预测一个片段是否是电的，然后通过将片段和预测组合起来重新包装袋子，以识别袋子中的设备。通过使用ALERT（爆炸物相关威胁的感知和定位）提供的数据进行的一系列实验，我们表明，如果以前见过类似的设备，该系统可以找到高比例的无监督分割设备，并显示出基于其组成部分的属性检测根本看不到的设备的有希望的结果。
英文摘要：
The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband in the minimum time possible. Automation and semi-automation of this procedure offers the potential to increase security by detecting more threats and improve the customer experience by speeding up the process. Traditional 2D x-ray images are often extremely difficult to examine due to the fact that they are tightly packed and contain a wide variety of cluttered and occluded objects. Because of these limitations, major airports are introducing 3D x-ray Computed Tomography (CT) baggage scanning. We investigate whether we can automate the process of detecting electric devices in these 3D images of luggage. Detecting electrical devices is of particular concern as they can be used to conceal explosives. Given the massive volume of luggage that needs to be screened for this threat, the best way to automate the detection is to first filter whether a bag contains an electric device or not, and if it does, to identify the number of devices and their location. We present an algorithm, Unpack, Predict, eXtract, Repack (UXPR), which involves unpacking through segmenting the data at a range of scales using an algorithm known as the Sieve, predicting whether a segment is electrical or not based on the histogram of voxel intensities, then repacking the bag by ensembling the segments and predictions to identify the devices in bags. Through a range of experiments using data provided by ALERT (Awareness and Localization of Explosives-Related Threats) we show that this system can find a high proportion of devices with unsupervised segmentation if a similar device has been seen before, and shows promising results for detecting devices not seen at all based on the properties of its constituent parts.
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