论文标题
在3D图像中检测电动设备
Detecting Electric Devices in 3D Images of Bags
论文作者
论文摘要
航空和运输保安行业面临着在可能的最短时间内筛查大量行李的威胁和违禁品的挑战。此过程的自动化和半自动化提供了通过检测更多威胁并通过加快流程来改善客户体验来提高安全性的潜力。传统的2D X射线图像通常很难检查，因为它们被紧密包装并包含各种混乱和遮挡的物体。由于这些局限性，主要机场正在引入3D X射线计算机断层扫描（CT）行李扫描。我们研究是否可以在这些行李的3D图像中自动检测电动设备的过程。检测电气设备特别关注，因为它们可用于隐藏炸药。鉴于需要筛选大量的行李，因此自动检测的最佳方法是首先过滤袋，无论是否包含电动设备，如果确实包含电动设备，则可以识别设备的数量及其位置。 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.通过使用警报提供的数据（与爆炸物相关的威胁的意识和本地化），我们表明，如果以前已经看到类似的设备，并且显示出有希望的结果，并且显示出基于其组成部分的特性，则显示出有希望的结果，并且显示出有希望的结果，并且显示出有希望的结果。
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.
