Detection of regular objects in baggage using multiple X-ray views

Abstract

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of a method based on multiple X-ray views to detect some regular prohibited items with very defined shapes and sizes. The method consists of two steps: 'structure estimation', to obtain a geometric model of the multiple views from the object to be inspected (baggage); and 'parts detection', to detect the parts of interest (prohibited items). The geometric model is estimated using a structure from a motion algorithm. The detection of the parts of interest is performed by an ad-hoc segmentation algorithm (object dependent) followed by a general tracking algorithm based on geometric and appearance constraints. In order to illustrate the effectiveness of the proposed method, experimental results on detecting regular objects − razor blades and guns − are shown yielding promising results.

Can laptops be left inside passenger bags if motion imaging is used in X-ray security screening?

“…Therefore, the outcomes of this study underline the appropriateness and importance of current regulations specifying that portable computers should be removed from passenger bags for X-ray screening. However, this might be reconsidered if effective and efficient automatic threat detection is available, which is particularly important for IEDs (see e.g., Singh and Singh, 2003; Eilbert, 2009; Mery et al, 2013). Furthermore, if more rotation in depth would be available, higher benefits could possibly be expected, which is of particular importance regarding new technological developments such as computer tomography offering 3D views.…”

Computer vision technology for X-ray testing

“…The use of multiple-view information yields Figure 9. Detection of a guns based on the trigger identification in multiple views [43] a significant improvement in performance because certain items are difficult to recognise using only one viewpoint [66] .…”

Applications in X-ray Testing

“…For example, synthesis of new X-ray images obtained from Kinetic Depth Effect X-ray (KDEX) images based on SIFT features in order to increase detection performance [49]; an approach for object detection in multiview dual-energy X-ray with promising preliminary results [54]; X-ray active vision that is able to adequate the viewpoint of the target object in order to obtain better X-ray images to analyze [61]; and tracking across multiple X-ray views in order to verify the diagnoses performed using a single view [5,[57][58][59]]. …”