Deep Fusion Driven Semantic Segmentation for the Automatic Recognition of Concealed Contraband Items

Muhammad Shafay, Taimur Hassan, Divya Velayudhan, Ernesto Damiani, Naoufel Werghi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Automatic detection of prohibited items in passenger baggage is a challenging task, especially in cluttered and occluded concealment scenarios. In this paper, we present a deep fusion driven semantic segmentation network that leverages multi-scale feature representations (extracted via CNN backbone) to generate highly accurate segmentation masks of the suspicious items irrespective of the clutter and concealment. Assessed with the public GDXray, SIXray and OPIXray datasets our proposed architecture reached a mean IoU performance of 0.7768, 0.6263, and 0.6713 respectively, outperforming the leading frameworks.

Original languageBritish English
Title of host publicationProceedings of the 12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020
EditorsAjith Abraham, Yukio Ohsawa, Niketa Gandhi, M. A. Jabbar, Abdelkrim Haqiq, Seán McLoone, Biju Issac
PublisherSpringer Science and Business Media Deutschland GmbH
Pages550-559
Number of pages10
ISBN (Print)9783030736880
DOIs
StatePublished - 2021
Event12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 - Virtual, Online
Duration: 15 Dec 202018 Dec 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1383 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020
CityVirtual, Online
Period15/12/2018/12/20

Keywords

  • Aviation security
  • Convolutional neural networks
  • Object recognition
  • Semantic segmentation
  • X-ray baggage imagery

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