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Recent advancements in Convolutional Neural Networks have yielded super-humanlevels of performance in image recognition tasks [13, 25]; however, withincreasing volumes of parcels crossing UK borders each year, classification ofthreats becomes integral to the smooth operation of UK borders. In this work wepropose the first pipeline to effectively process Dual-Energy X-Ray scanneroutput, and perform classification capable of distinguishing between firearmfamilies (Assault Rifle, Revolver, Self-Loading Pistol,Shotgun, and Sub-MachineGun) from this output. With this pipeline we compare re-cent ConvolutionalNeural Network architectures against the X-Ray baggage domain via TransferLearning and show ResNet50 to be most suitable to classification - outlining anumber of considerations for operational success within the domain.

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