CH-Net: Deep adversarial autoencoders for semantic segmentation in X-ray images of cabin baggage screening at airports

Mohamed Chouai (), Mostefa Merah () and Malika Mimi ()
Additional contact information
Mohamed Chouai: Mostaganem University
Mostefa Merah: Mostaganem University
Malika Mimi: Mostaganem University

Journal of Transportation Security, 2020, vol. 13, issue 1, No 5, 89 pages

Abstract: Abstract Billions of suitcases and other belongings are checked every year in the X-ray systems in airports around the world. This process is of great importance because it involves the detection of possible dangerous objects such as weapons and explosives. However, the work done by airport surveillance personnel is not free from errors, usually due to tiredness or distractions. This is a security problem that can always be reduced with the help of automatic intelligent tools. This paper attempts to make a contribution to the field of object recognition in X-ray testing for luggage control by proposing a deep learning system that combines a deep convolutional network with an adversarial autoencoder acting as a powerful feature extractor mechanism. The system is developed to separate transmission X-ray images into potentially overlapping regions, separating the X-ray image into organic and inorganic images, taking into consideration the overlapping between the same and different types of materials. To show the superiority of our proposed system, a comparative analysis was carried out including the state-of-the-art deep learning semantic segmentation systems. The proposed method demonstrated highly promising results, achieving the best performance in global accuracy, mean boundary F1 and mean IoU, with a percentage of 80.17%, 76.28% and 76.84%, respectively.

Keywords: Transport security; Baggage control; Semantic segmentation; Deep learning; Autoencoder (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12198-020-00211-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jtrsec:v:13:y:2020:i:1:d:10.1007_s12198-020-00211-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12198

DOI: 10.1007/s12198-020-00211-5

Access Statistics for this article

Journal of Transportation Security is currently edited by Andrew Thomas

More articles in Journal of Transportation Security from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().