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Trainable structure tensors for autonomous baggage threat detection under extreme occlusion
  • Taimur Hassan, Center for Cyber-Physical Systems (C2PS), Khalifa University, United Arab Emirates
  • Samet Akçay, Department of Computer Science, Durham University, United Kingdom
  • Mohammed Bennamoun, Department of Computer Science and Software Engineering, University of Western Australia, Australia
  • Salman Khan, Mohamed bin Zayed University of Artificial Intelligence
  • Naoufel Werghi, Center for Cyber-Physical Systems (C2PS), Khalifa University, United Arab Emirates
Document Type

Detecting baggage threats is one of the most difficult tasks, even for expert officers. Many researchers have developed computer-aided screening systems to recognize these threats from the baggage X-ray scans. However, all of these frameworks are limited in identifying the contraband items under extreme occlusion. This paper presents a novel instance segmentation framework that utilizes trainable structure tensors to highlight the contours of the occluded and cluttered contraband items (by scanning multiple predominant orientations), while simultaneously suppressing the irrelevant baggage content. The proposed framework has been extensively tested on four publicly available X-ray datasets where it outperforms the state-of-the-art frameworks in terms of mean average precision scores. Furthermore, to the best of our knowledge, it is the only framework that has been validated on combined grayscale and colored scans obtained from four different types of X-ray scanners. Copyright © 2020, The Authors. All rights reserved.

Publication Date
  • Computer Vision and Pattern Recognition (cs.CV),
  • Image and Video Processing (eess.IV)

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC by 4.0

Uploaded 21 July 2022

Citation Information
T. Hassan, S. Akcay, M. Bennamoun, S. Khan, and N. Werghi, "Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion", 2020, arXiv:2009.13158