Detecting Prohibited Items in X-Ray Images: A Contour Proposal Learning Approach

Taimur Hassan, Meriem Bettayeb, Samet Akcay, Salman Khan, Mohammed Bennamoun, Naoufel Werghi

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

53 Scopus citations

Abstract

X-ray baggage screening plays a vital role in aviation security. Manual inspection of potentially anomalous items is challenging due to the clutter and occlusion within Xray scans. Here, we address this issue by presenting an object-boundaries driven framework for the automated detection of suspicious items from X-ray baggage scans. Rather than recognizing objects directly from the X-ray images, our two-stage detection approach first extracts contour-based proposals using a novel cascaded structure tensor technique and subsequently passes the candidate proposals to a single feed-forward convolutional neural network for recognition. Thorough experimentation on GDXray and SIXray datasets demonstrates that the proposed model achieves a mean area under the curve of 0.9878, outperforming the existing renown state-of-the-art object detection frameworks.

Original languageBritish English
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages2016-2020
Number of pages5
ISBN (Electronic)9781728163956
DOIs
StatePublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sep 202028 Sep 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/09/2028/09/20

Keywords

  • Baggage screening
  • Convolution Neural Network
  • Image analysis
  • Structure Tensor
  • X-ray images

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