Citation

Vukadinovic D, Osés MR, Anderson D. J. Transp. Secur. 2023; 16(1): e3.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s12198-023-00261-5

PMID

unavailable

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.

Language: en

Keywords

Aviation security; Detection; Explosives; Inorganic powders; X-ray