Modern computer vision techniques for X-Ray testing in baggage inspection

Domingo Mery, Erick Svec, Marco Arias, Vladimir Riffo, Jose M. Saavedra, Sandipan Banerjee

Research output: Contribution to journalArticlepeer-review

144 Scopus citations

Abstract

X-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.

Original languageEnglish
Article number7775025
Pages (from-to)682-692
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume47
Issue number4
DOIs
StatePublished - Apr 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Baggage screening
  • deep learning
  • implicit shape model (ISM)
  • object categorization
  • object detection
  • object recognition
  • sparse representations
  • threat objects
  • X-ray testing

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