  Detecting prohibited items in X-ray security imagery is pivotal in
maintaining border and transport security against a wide range of threat
profiles. Convolutional Neural Networks (CNN) with the support of a significant
volume of data have brought advancement in such automated prohibited object
detection and classification. However, collating such large volumes of X-ray
security imagery remains a significant challenge. This work opens up the
possibility of using synthetically composed imagery, avoiding the need to
collate such large volumes of hand-annotated real-world imagery. Here we
investigate the difference in detection performance achieved using real and
synthetic X-ray training imagery for CNN architecture detecting three exemplar
prohibited items, {Firearm, Firearm Parts, Knives}, within cluttered and
complex X-ray security baggage imagery. We achieve 0.88 of mean average
precision (mAP) with a Faster R-CNN and ResNet-101 CNN architecture for this
3-class object detection using real X-ray imagery. While the performance is
comparable with synthetically composited X-ray imagery (0.78 mAP), our extended
evaluation demonstrates both challenge and promise of using synthetically
composed images to diversify the X-ray security training imagery for automated
detection algorithm training.
