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The aviation and transport security industries face the challenge ofscreening high volumes of baggage for threats and contraband in the minimumtime possible. Automation and semi-automation of this procedure offers thepotential to increase security by detecting more threats and improve thecustomer experience by speeding up the process. Traditional 2D x-ray images areoften extremely difficult to examine due to the fact that they are tightlypacked and contain a wide variety of cluttered and occluded objects. Because ofthese limitations, major airports are introducing 3D x-ray Computed Tomography(CT) baggage scanning. We investigate whether we can automate the process ofdetecting electric devices in these 3D images of luggage. Detecting electricaldevices 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 bagcontains an electric device or not, and if it does, to identify the number ofdevices and their location. We present an algorithm, Unpack, Predict, eXtract,Repack (UXPR), which involves unpacking through segmenting the data at a rangeof scales using an algorithm known as the Sieve, predicting whether a segmentis electrical or not based on the histogram of voxel intensities, thenrepacking the bag by ensembling the segments and predictions to identify thedevices in bags. Through a range of experiments using data provided by ALERT(Awareness and Localization of Explosives-Related Threats) we show that thissystem can find a high proportion of devices with unsupervised segmentation ifa similar device has been seen before, and shows promising results fordetecting devices not seen at all based on the properties of its constituentparts.

Detecting Electric Devices in 3D Images of Bags