  Recent advancements in Convolutional Neural Networks have yielded super-human
levels of performance in image recognition tasks [13, 25]; however, with
increasing volumes of parcels crossing UK borders each year, classification of
threats becomes integral to the smooth operation of UK borders. In this work we
propose the first pipeline to effectively process Dual-Energy X-Ray scanner
output, and perform classification capable of distinguishing between firearm
families (Assault Rifle, Revolver, Self-Loading Pistol,Shotgun, and Sub-Machine
Gun) from this output. With this pipeline we compare re-cent Convolutional
Neural Network architectures against the X-Ray baggage domain via Transfer
Learning and show ResNet50 to be most suitable to classification - outlining a
number of considerations for operational success within the domain.
