Automated deep learning for threat detection in luggage from x-ray images

Alessio Petrozziello, Ivan Jordanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Luggage screening is a very important part of the airport security risk assessment and clearance process. Automating the threat objects detection from x-ray scans of passengers’ luggage can speed-up and increase the efficiency of the whole security procedure. In this paper we investigate and compare several algorithms for detection of firearm parts in x-ray images of travellers’ baggage. In particular, we focus on identifying steel barrel bores as threat objects, being the main part of the weapon needed for deflagration. For this purpose, we use a dataset of 22k double view x-ray scans, containing a mixture of benign and threat objects. In the pre-processing stage we apply standard filtering techniques to remove noisy and ambiguous images (i.e., smoothing, black and white thresholding, edge detection, etc.) and subsequently employ deep learning techniques (Convolutional Neural Networks and Stacked Autoencoders) for the classification task. For comparison purposes we also train and simulate shallow Neural Networks and Random Forests algorithms for the objects detection. Furthermore, we validate our findings on a second dataset of double view x-ray scans of courier parcels. We report and critically discuss the results of the comparison on both datasets, showing the advantages of our approach.
Original languageEnglish
Title of host publicationSEA 2019: Analysis of Experimental Algorithms
EditorsIlias Kotsireas, Panos Pardalos, Konstantinos E. Parsopoulos, Dimitris Souravlias, Arsenis Tsokas
PublisherSpringer
Pages505-512
Number of pages5
ISBN (Electronic)978-3-030-34029-2
ISBN (Print)978-3-030-34028-5
DOIs
Publication statusPublished - 14 Nov 2019
EventSEA 2019: International Symposium on Experimental Algorithms - Kalamata, Greece
Duration: 24 Jun 201929 Jun 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11544
ISSN (Print)0302-9743

Conference

ConferenceSEA 2019
CountryGreece
CityKalamata
Period24/06/1929/06/19

Keywords

  • Baggage screening
  • Deep Learning
  • Convolutional Neural Networks
  • Image filtering
  • X-ray Images

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