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On the use of deep learning for the detection of firearms in x-ray baggage security imagery.

Gaus, Y.F.A. and Bhowmik, N. and Breckon, T.P. (2019) 'On the use of deep learning for the detection of firearms in x-ray baggage security imagery.', in Proceeding of the International Symposium on Technologies for Homeland Security. Piscataway, NJ: IEEE, pp. 1-7.

Abstract

X-ray imagery security screening is essential to maintaining transport security against a varying profile of prohibited items. Particular interest lies in the automatic detection and classification of prohibited items such as firearms and firearm components within complex and cluttered X-ray security imagery. We address this problem by exploring various end-to-end object detection Convolutional Neural Network (CNN) architectures. We evaluate several leading variants spanning the Faster R-CNN, Mask R-CNN, and RetinaNet architectures. Overall, we achieve maximal detection performance using a Faster R-CNN architecture with a ResNet 101 classification network, obtaining 0.91 and 0.88 of mean Average Precision (mAP) for a two-class problem from varying X-ray imaging dataset. Our results offer very low false positive (FP) complimented by a high accuracy (A) $(\mathrm{FP}=0.00\%,\ \mathrm{A}=99.96\%)$ . This result illustrates the applicability and superiority of such integrated region based detection models within this X-ray security imagery context.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/HST47167.2019.9032917
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:30 August 2019
Date deposited:02 January 2020
Date of first online publication:12 March 2020
Date first made open access:19 March 2020

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