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Evaluating a dual convolutional neural network architecture for object-wise anomaly detection in cluttered x-ray security imagery.

Gaus, Y.F.A. and Bhowmik, N. and Akcay, A. and Guillen-Garcia, P.M. and Barker, J.W and Breckon, T.P. (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. .

Abstract

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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/IJCNN.2019.8851829
Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:07 March 2019
Date deposited:04 April 2019
Date of first online publication:14 July 2019
Date first made open access:13 November 2019

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