DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery

Authors: Ashley C Williamson, Patrick Dickinson, Tryphon Lambrou, John C Murray

Abstract: 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 recent 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.

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