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On the impact of object and sub-component level segmentation strategies for supervised anomaly detection within x-ray security imagery.

Bhowmik, N. and Gaus, Y.F.A. and Akcay, S. and Barker, J.W. and Breckon, T.P. (2019) 'On the impact of object and sub-component level segmentation strategies for supervised anomaly detection within x-ray security imagery.', in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019. Piscataway, NJ: IEEE, pp. 986-991.

Abstract

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within complex electronic items. Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. Relative performance is evaluated over an extensive dataset of exemplar cluttered X-ray imagery, with a focus on consumer electronics items. We find that sub-component level segmentation produces marginally superior performance in the secondary anomaly detection via classification stage, with true positive of ~98% of anomalies, with a ~3% false positive.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/ICMLA.2019.00168
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:21 September 2019
Date deposited:29 December 2019
Date of first online publication:17 February 2020
Date first made open access:04 June 2020

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