Automated detection of contraband items in X-ray images can significantly
increase public safety, by enhancing the productivity and alleviating the
mental load of security officers in airports, subways, customs/post offices,
etc. The large volume and high throughput of passengers, mailed parcels, etc.,
during rush hours practically make it a Big Data problem. Modern computer
vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of
undertaking this task even under resource-constrained and embedded execution
scenarios, e.g., as is the case with fast, single-stage object detectors.
However, no comparative experimental assessment of the various relevant DNN
components/methods has been performed under a common evaluation protocol, which
means that reliable cross-method comparisons are missing. This paper presents
exactly such a comparative assessment, utilizing a public relevant dataset and
a well-defined methodology for selecting the specific DNN components/modules
that are being evaluated. The results indicate the superiority of Transformer
detectors, the obsolete nature of auxiliary neural modules that have been
developed in the past few years for security applications and the efficiency of
the CSP-DarkNet backbone CNN.

自动检测 X 射线图像中的违禁物品可以显著增加公共安全，提高机场、地铁、海关 / 邮局等地的安全人员的工作效率，并减轻其心理负担。研究表明，变压器检测器优越于过去几年为安全应用开发的辅助神经模块和 CSP-DarkNet 骨干卷积神经网络十分高效。