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Automated comparison of X-ray images for cargo scanning

: Visser, W.; Schwaninger, A.; Hardmeier, D.; Flisch, A.; Costin, M.; Vienne, C.; Sukowski, F.; Hassler, U.; Dorion, I.; Marciano, A.; Koomen, G.; Slegt, M.; Canonica, A.C.


Claycomb, W.R. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Carnahan Conference on Security Technology, ICCST 2016. Proceedings : 24-27 October, 2016, Orlando, Florida, USA
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-1070-7
ISBN: 978-1-5090-1072-1
ISBN: 978-1-5090-1073-8
ISBN: 1-5090-1073-4
International Carnahan Conference on Security Technology (ICCST) <50, 2016, Orlando/Fla.>
Fraunhofer IIS ()

Customs administrations are responsible for the enforcement of fiscal integrity and security of movements of goods across land and sea borders. In order to verify whether the transported goods match the transport declaration, X-ray imaging of containers is used at many customs site worldwide. The main objective of the research and development project 'Automated Comparison of X-ray Images for Cargo Scanning (ACXIS)', which is funded by the European 7th Framework Program, is to improve the efficiency and effectiveness of the inspection procedures of cargo at customs using X-ray technology. The current inspection procedures are reviewed to identify risks, catalogue illegal cargo, and prioritize detection scenarios. Based on these results, we propose an integrated solution that provides automation, information exchange between customs administrations, and computer-based training modules for customs officers. Automated target recognition (ATR) functions analyze the X-ray ima ge after a scan is made to detect certain types of goods such as cigarettes, weapons and drugs in the freight or container. Other helpful information can also be provided, such as the load homogeneity, total or partial weight, or the number of similar items. The ATR functions are provided as an option to the user. The X-ray image is transformed into a manufacturer-independent format through geometrical and spectral corrections and stored into a database along with the user feedback and other related data. This information can be exchanged with similar systems at other sites, thus facilitating information exchange between customs administrations. The database is seeded with over 30'000 examples of legitimate and illegal goods. These examples are used by the ATR functions through machine learning techniques, which are further strengthened by the information exchange.