On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Gaus, Y.F.A.; Bhowmik, N.; Breckon, T.P.

doi:10.1109/hst47167.2019.9032917

On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Gaus, Y.F.A.; Bhowmik, N.; Breckon, T.P.

Authors

Y.F.A. Gaus

N. Bhowmik

Professor Toby Breckon toby.breckon@durham.ac.uk
Professor

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.

Citation

Gaus, Y., Bhowmik, N., & Breckon, T. (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 (1-7). https://doi.org/10.1109/hst47167.2019.9032917

Conference Name	2019 IEEE International Symposium on Technologies for Homeland Security
Conference Location	Boston, USA
Start Date	Nov 5, 2019
End Date	Nov 6, 2019
Acceptance Date	Aug 30, 2019
Online Publication Date	Mar 12, 2020
Publication Date	Nov 5, 2019
Deposit Date	Dec 20, 2019
Publicly Available Date	Mar 19, 2020
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-7
Book Title	Proceeding of the International Symposium on Technologies for Homeland Security.
DOI	https://doi.org/10.1109/hst47167.2019.9032917

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