Rogers, TW;
Jaccard, N;
Protonotarios, ED;
Ollier, J;
Morton, EJ;
Griffin, LD;
(2017)
Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines.
In: Claycomb, WR, (ed.)
2016 IEEE International Carnahan Conference on Security Technology (ICCST): Proceedings.
(pp. pp. 283-289).
IEEE: New York, USA.
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Abstract
We propose a framework for Threat Image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations; (ii) magnification; (iii) rotations; (iv) noise; (v) illumination; (vi) volume and density; and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical. We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening.
| Type: | Proceedings paper |
|---|---|
| Title: | Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines |
| Event: | IEEE International Carnahan Conference on Security Technology (ICCST), 24-27 October 2016, Orlando, Florida, USA |
| Location: | Orlando, FL |
| Dates: | 24 October 2016 - 27 October 2016 |
| ISBN-13: | 9781509010707 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CCST.2016.7815717 |
| Publisher version: | https://doi.org/10.1109/CCST.2016.7815717 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | X-ray imaging, Containers, Training, Lighting, Detectors, Visualization, Robustness |
| UCL classification: | UCL 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: | https://discovery.ucl.ac.uk/id/eprint/1568939 |
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