Automatic Detection of Threat Objects in X-ray Baggage Inspection Using Mask Region-Based Convolutional Neural Network and Deformable Convolutional Network-Reference-Cited by-同舟云学术

Automatic Detection of Threat Objects in X-ray Baggage Inspection Using Mask Region-Based Convolutional Neural Network and Deformable Convolutional Network

Published:2022-12 Issue:12 Volume:58 Page:1175-1184
ISSN:1061-8309
Container-title:Russian Journal of Nondestructive Testing
language:en
Short-container-title:Russ J Nondestruct Test

Author:

Kim Jongchol,Ri Jinmyong,Jo Hyok

Publisher

Pleiades Publishing Ltd

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science

Link

https://link.springer.com/content/pdf/10.1134/S1061830922600733.pdf

Reference36 articles.

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2. Akcay, S., Kundegorski, M.E., Devereux, M., and Breckon, T.P., Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery, IEEE Int. Conf. Image Process. (ICIP) (Phoenix, 2016), pp. 1057–1061. https://doi.org/10.1109/ICIP.2016.7532519

3. Schwaninger, A., Bolfing, A., Halbherr, T., Helman, S., Belyavin, A., and Hay, L., The impact of image based factors and training on threat detection performance in X-ray screening, Proc. Int. Conf. Res. Air Transp. (Fairfax, 2008), pp. 317–324. https://doi.org/10.13140/RG.2.1.1299.3526

4. Michel, S., Koller, S.M., de Ruiter, J.C., Moerland, R., Hogervorst, M., and Schwaninger, A., Computer-based training increases efficiency in X-ray image interpretation by aviation security screeners, Proc. 41st Annu. IEEE Int. Carnahan Conf. Secur. Technol. (Ottawa, 2007), pp. 201–206. https://doi.org/10.1109/CCST.2007.4373490

5. Turcsany, D., Mouton, A., and Breckon, T.P., Improving feature-based object recognition for X-ray baggage security screening using primed visual words, IEEE Int. Conf. Industr. Technol. (ICIT) (Capetown, 2013), pp. 1140–1145. https://doi.org/10.1109/ICIT.2013.6505833

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