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Does the application of virtually merged images influence the effectiveness of computer-based training in x-ray screening?

机译:几乎合并图像的应用是否会影响基于计算机的X射线筛选培训的有效性?

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The necessity of computer based training for airport security screening officers to achieve and maintain a high level of x-ray image interpretation competency is well-known. During such training, x-ray images of passenger bags, similar to how they appear at the security checkpoint, are presented to the screening officers on a screen and have to be judged regarding their contents. Certain computer based training systems, such as X-Ray Tutor (XRT), apply a special algorithm that automatically merges images of fictional threat items (FTIs) into x-ray images of passenger bags. The advantage of virtual image merging is that a) a huge variety of different bag images containing different threats can be created, and b) the difficulty of threats (e.g. viewpoint, superposition and bag complexity) can be adapted to individual performance and learning progress. However, merging images virtually can lead to artifacts appearing on the resulting pictures. Therefore, the question arises if the training with merged images actually reflects the reality or if the resulting artifacts on the images actually make it easier to detect threat items during training. If this would be the case, the actual effectiveness of such a CBT would have to be questioned. The aim of this study was to investigate whether threat items that are virtually merged into bags (as they appear during training), are detected more easily than threat items physically embedded in bags and x-rayed as a whole (like at the security checkpoint). A test was conducted with screeners at different international airports. 256 images of passenger bags were presented to screeners, 128 of them contained threat items. Half of these threat images were created through virtual merging, while for the other half of the images threat items had been placed physically into the bags. Half of the used threat items were familiar to the screeners from training (XRT-library), whereas the other half of the items were new. The same study was rep- - licated one year later. In both studies, the screeners achieved high detection performance scores in the test. However, only very small differences in detection performance for the virtually merged threat items and the physically embedded threat items were found. In fact, detection performance was even slightly higher for the physically embedded threat items. In summary, results imply that when well elaborated merging algorithms are used, small artifacts appearing on the test images influence neither the effectiveness of CBT nor the detection of real threat items in real x-ray images negatively.
机译:基于计算机的机场安全筛选人员培训的必要性实现和维持高水平的X射线图像解释能力是众所周知的。在这种训练期间,乘客袋的X射线图像类似于它们在安全检查点上出现的速度图像,呈现给屏幕上的筛选人员,并且必须判断他们的内容。基于计算机的基于计算机的训练系统,例如X射线导师(XRT),应用一种特殊算法,该算法自动将虚构威胁物品(FTI)的图像合并到乘客袋的X射线图像中。虚拟图像合并的优点是A)可以创建包含不同威胁的巨大不同袋图像,B)威胁的难度(例如,观点,叠加和袋子复杂性)可以适应各个性能和学习进度。但是,合并图像几乎可以导致出现在所产生的图片上的伪影。因此,如果具有合并图像的培训实际反映现实或图像上产生的伪像实际上,问题出现了问题,或者在训练期间更容易检测威胁项目。如果是这种情况,这种CBT的实际有效性必须受到质疑。本研究的目的是调查几乎合并到袋子的威胁项目(在培训期间出现),比物理嵌入在袋子和整个X射线的威胁物品(如安全检查点) 。在不同国际机场的筛选人进行了测试。 256个乘客袋的图像呈现给筛选者,其中128个包含威胁物品。通过虚拟合并创建了一半的这些威胁图像,而对于图片的其他一半,威胁物品已经物理地放入袋中。来自培训(XRT-Library)的筛选器熟悉一半的威胁物品,而其他一半的物品是新的。同样的研究是一年后获得的。在这两项研究中,筛选者在测试中实现了高的检测性能分数。然而,发现了几乎合并威胁项目的检测性能和物理嵌入式威胁项目的检测性能差异非常小。实际上,对于物理嵌入式威胁物品,检测性能甚至略高。总之,结果意味着当使用良好的精细合并算法时,出现在测试图像上的小型伪像既不会影响真实X射线图像中的真实威胁项目的有效性。

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