  X-ray imagery security screening is essential to maintaining transport
security against a varying profile of threat or prohibited items. Particular
interest lies in the automatic detection and classification of weapons such as
firearms and knives within complex and cluttered X-ray security imagery. Here,
we address this problem by exploring various end-to-end object detection
Convolutional Neural Network (CNN) architectures. We evaluate several leading
variants spanning the Faster R-CNN, Mask R-CNN, and RetinaNet architectures to
explore the transferability of such models between varying X-ray scanners with
differing imaging geometries, image resolutions and material colour profiles.
Whilst the limited availability of X-ray threat imagery can pose a challenge,
we employ a transfer learning approach to evaluate whether such inter-scanner
generalisation may exist over a multiple class detection problem. Overall, we
achieve maximal detection performance using a Faster R-CNN architecture with a
ResNet$_{101}$ classification network, obtaining 0.88 and 0.86 of mean Average
Precision (mAP) for a three-class and two class item from varying X-ray imaging
sources. Our results exhibit a remarkable degree of generalisability in terms
of cross-scanner performance (mAP: 0.87, firearm detection: 0.94 AP). In
addition, we examine the inherent adversarial discriminative capability of such
networks using a specifically generated adversarial dataset for firearms
detection - with a variable low false positive, as low as 5%, this shows both
the challenge and promise of such threat detection within X-ray security
imagery.
