Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection

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dc.contributor.authorMery Quiroz, Domingo
dc.contributor.authorSvec, Erick
dc.contributor.authorArias Figueroa, Marco Antonio
dc.contributor.authorRiffo, V.
dc.contributor.authorSaavedra, J. M.
dc.contributor.authorBanerjee, S.
dc.date.accessioned2022-05-18T14:39:52Z
dc.date.available2022-05-18T14:39:52Z
dc.date.issued2017
dc.description.abstractX-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items, such as handguns and explosives. Generally, human operators are in charge of these tasks as automated recognition in baggage inspection is still far from perfect. Research and development on X-ray testing is, however, exploring new approaches based on computer vision that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition in X-ray testing by evaluating different computer vision strategies that have been proposed in the last years. We tested ten approaches. They are based on bag of words, sparse representations, deep learning, and classic pattern recognition schemes among others. For each method, we: 1) present a brief explanation; 2) show experimental results on the same database; and 3) provide concluding remarks discussing pros and cons of each method. In order to make fair comparisons, we define a common experimental protocol based on training, validation, and testing data (selected from the public GDXray database). The effectiveness of each method was tested in the recognition of three different threat objects: 1) handguns; 2) shuriken (ninja stars); and 3) razor blades. In our experiments, the highest recognition rate was achieved by methods based on visual vocabularies and deep features with more than 95% of accuracy. We strongly believe that it is possible to design an automated aid for the human inspection task using these computer vision algorithms.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/TSMC.2016.2628381
dc.identifier.issn2168-2232
dc.identifier.urihttps://doi.org/10.1109/TSMC.2016.2628381
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7775025
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64186
dc.information.autorucEscuela de ingeniería ; Mery Quiroz, Domingo ; S/I ; 102382
dc.information.autorucEscuela de ingeniería ; Svec, Erick ; S/I ; 148410
dc.information.autorucEscuela de ingeniería ; Arias Figueroa, Marco Antonio ; S/I ; 185444
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final692
dc.pagina.inicio682
dc.revistaIEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.rightsacceso restringido
dc.subjectX-ray imaging
dc.subjectComputer vision
dc.subjectTesting
dc.subjectInspection
dc.subjectObject recognition
dc.subjectImage recognition
dc.subjectWeapons
dc.titleModern Computer Vision Techniques for X-Ray Testing in Baggage Inspectiones_ES
dc.typeartículo
dc.volumen47
sipa.codpersvinculados102382
sipa.codpersvinculados148410
sipa.codpersvinculados185444
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