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Automated detection of inorganic powders in X-ray images of airport luggage

Author

Listed:
  • Danijela Vukadinovic

    (European Commission)

  • Miguel Ruiz Osés

    (European Commission)

  • David Anderson

    (European Commission)

Abstract

At the checkpoint, the detection of illicit inorganic powders in passenger luggage using conventional X-ray can be challenging. An algorithm is presented for the automated detection of inorganic powder-like substances from complex X-ray images of highly cluttered passenger bags using computer vision. The proposed method utilizes support vector machine (SVM) classifiers built from local binary patterns (LBP) texture features. When tested on a dataset created in-house, the algorithm achieves a detection precision of 97% and a false positive rate of 3%. This is the first study performed on a realistic dataset, including different amounts and shapes of powders and electronic clutter, and where the success of the automated method is compared with inter-observer variability.

Suggested Citation

  • Danijela Vukadinovic & Miguel Ruiz Osés & David Anderson, 2023. "Automated detection of inorganic powders in X-ray images of airport luggage," Journal of Transportation Security, Springer, vol. 16(1), pages 1-28, December.
    • Handle: RePEc:spr:jtrsec:v:16:y:2023:i:1:d:10.1007_s12198-023-00261-5
    DOI: 10.1007/s12198-023-00261-5
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    References listed on IDEAS

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    1. Mohamed Chouai & Mostefa Merah & José-Luis Sancho-Gómez & Malika Mimi, 2020. "Supervised feature learning by adversarial autoencoder approach for object classification in dual X-ray image of luggage," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1101-1112, June.
    2. Cesar Niyomugabo & Hyo-rim Choi & Tae Yong Kim, 2016. "A Modified Adaboost Algorithm to Reduce False Positives in Face Detection," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-6, September.
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