  X-ray baggage security screening is widely used to maintain aviation and
transport security. Of particular interest is the focus on automated security
X-ray analysis for particular classes of object such as electronics, electrical
items, and liquids. However, manual inspection of such items is challenging
when dealing with potentially anomalous items. Here we present a dual
convolutional neural network (CNN) architecture for automatic anomaly detection
within complex security X-ray imagery. We leverage recent advances in
region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures
such as RetinaNet to provide object localisation variants for specific object
classes of interest. Subsequently, leveraging a range of established CNN object
and fine-grained category classification approaches we formulate within object
anomaly detection as a two-class problem (anomalous or benign). While the best
performing object localisation method is able to perform with 97.9% mean
average precision (mAP) over a six-class X-ray object detection problem,
subsequent two-class anomaly/benign classification is able to achieve 66%
performance for within object anomaly detection. Overall, this performance
illustrates both the challenge and promise of object-wise anomaly detection
within the context of cluttered X-ray security imagery.
