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Transferring X-ray based automated threat detection between scanners with different energies and resolution

Caldwell, M; Ransley, M; Rogers, TW; Griffin, LD; (2017) Transferring X-ray based automated threat detection between scanners with different energies and resolution. In: Bouma, H and Carlysle-Davies, F and Stokes, RJ and Yitzhaky, Y, (eds.) Counterterrorism, Crime Fighting, Forensics and Surveillance Technologies. (pp. 104410F:1-104410F:10). Society of Photo-Optical Instrumentation Engineers (SPIE): Bellingham, Washington, USA. Green open access

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Abstract

A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners.

Type: Proceedings paper
Title: Transferring X-ray based automated threat detection between scanners with different energies and resolution
Event: SPIE Security + Defence 2017, 11-14 September 2017, Warsaw, Poland
Location: Warsaw, POLAND
Dates: 11 September 2017 - 12 September 2017
ISBN-13: 9781510613461
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2277641
Publisher version: http://dx.doi.org/10.1117/12.2277641
Language: English
Additional information: This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep Learning, X-ray imaging, Automated Threat Detection, Transfer Learning
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10041415
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