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Detecting prohibited items in X-ray security imagery is pivotal inmaintaining border and transport security against a wide range of threatprofiles. Convolutional Neural Networks (CNN) with the support of a significantvolume of data have brought advancement in such automated prohibited objectdetection and classification. However, collating such large volumes of X-raysecurity imagery remains a significant challenge. This work opens up thepossibility of using synthetically composed imagery, avoiding the need tocollate such large volumes of hand-annotated real-world imagery. Here weinvestigate the difference in detection performance achieved using real andsynthetic X-ray training imagery for CNN architecture detecting three exemplarprohibited items, {Firearm, Firearm Parts, Knives}, within cluttered andcomplex X-ray security baggage imagery. We achieve 0.88 of mean averageprecision (mAP) with a Faster R-CNN and ResNet-101 CNN architecture for this3-class object detection using real X-ray imagery. While the performance iscomparable with synthetically composited X-ray imagery (0.78 mAP), our extendedevaluation demonstrates both challenge and promise of using syntheticallycomposed images to diversify the X-ray security training imagery for automateddetection algorithm training.

The Good, the Bad and the Ugly: Evaluating Convolutional Neural Networks  for Prohibited Item Detection Using Real and Synthetically Composited X-ray  Imagery