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Durham University

Computer Science


Publication details for Professor Toby Breckon

Gaus, Y.F.A., Bhowmik, N., Akcay, A., Guillen-Garcia, P.M., Barker, J.W & Breckon, T.P. (2019), Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery, Joint Conference on Neural Networks. Budapest, Hungary, IEEE.

Author(s) from Durham


X-ray baggage security screening is widely used to
maintain aviation and transport secure. 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). Whilst 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.