Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Gaus, Y.F.A.; Bhowmik, N.; Akcay, A.; Guillen-Garcia, P.M.; Barker, J.W; Breckon, T.P.
Professor Toby Breckon email@example.com
X-ray baggage security screening is widely used to maintain aviation and transport secure. Of particular interestis 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.
Gaus, Y., Bhowmik, N., Akcay, A., Guillen-Garcia, P., Barker, J., & Breckon, T. (2019). Evaluating a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery. In 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings. https://doi.org/10.1109/ijcnn.2019.8851829
|Conference Name||Proc. Int. Joint Conference on Neural Networks|
|Conference Location||Budapest, Hungary|
|Acceptance Date||Mar 7, 2019|
|Online Publication Date||Jul 14, 2019|
|Deposit Date||Apr 2, 2019|
|Publicly Available Date||Nov 13, 2019|
|Book Title||2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings.|
Accepted Conference Proceeding
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