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Recent Advances in Baggage Threat Detection: A Comprehensive and Systematic Survey

Published: 23 December 2022 Publication History

Abstract

X-ray imagery systems have enabled security personnel to identify potential threats contained within the baggage and cargo since the early 1970s. However, the manual process of screening the threatening items is time-consuming and vulnerable to human error. Hence, researchers have utilized recent advancements in computer vision techniques, revolutionized by machine learning models, to aid in baggage security threat identification via 2D X-ray and 3D CT imagery. However, the performance of these approaches is severely affected by heavy occlusion, class imbalance, and limited labeled data, further complicated by ingeniously concealed emerging threats. Hence, the research community must devise suitable approaches by leveraging the findings from existing literature to move in new directions. Towards that goal, we present a structured survey providing systematic insight into state-of-the-art advances in baggage threat detection. Furthermore, we also present a comprehensible understanding of X-ray-based imaging systems and the challenges faced within the threat identification domain. We include a taxonomy to classify the approaches proposed within the context of 2D and 3D CT X-ray-based baggage security threat screening and provide a comparative analysis of the performance of the methods evaluated on four benchmarks. Besides, we also discuss current open challenges and potential future research avenues.

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cover image ACM Computing Surveys
ACM Computing Surveys  Volume 55, Issue 8
August 2023
789 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3567473
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New York, NY, United States

Publication History

Published: 23 December 2022
Online AM: 20 July 2022
Accepted: 04 July 2022
Revised: 17 April 2022
Received: 24 September 2021
Published in CSUR Volume 55, Issue 8

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Author Tags

  1. Baggage screening
  2. computer vision
  3. deep learning
  4. 2D X-ray and 3D CT X-ray security screening

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  • Survey
  • Refereed

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  • Khalifa University
  • Abu Dhabi Department of Education and Knowledge (ADEK)

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  • (2025)Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection Under Noisy AnnotationsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351654620(234-248)Online publication date: 1-Jan-2025
  • (2024)Semi-supervised contour-driven broad learning system for autonomous segmentation of concealed prohibited baggage itemsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-024-00182-77:1Online publication date: 24-Dec-2024
  • (2024)Autonomous Localization of X-Ray Baggage Threats via Weakly Supervised LearningIEEE Transactions on Industrial Informatics10.1109/TII.2023.334883820:4(6563-6572)Online publication date: Apr-2024
  • (2024)A Cross-Domain Threat Screening and Localization Framework Using Vision Transformers and Self-supervised Learning2024 14th International Conference on Pattern Recognition Systems (ICPRS)10.1109/ICPRS62101.2024.10677838(1-7)Online publication date: 15-Jul-2024
  • (2024)CLIFS: Clip-Driven Few-Shot Learning for Baggage Threat Classification2024 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP51287.2024.10647879(753-759)Online publication date: 27-Oct-2024
  • (2024)A Cross-domain Vision Transformer Based Framework for Baggage Threat Classification2024 16th International Conference on Computer and Automation Engineering (ICCAE)10.1109/ICCAE59995.2024.10569367(493-497)Online publication date: 14-Mar-2024
  • (2024)Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00320(3142-3152)Online publication date: 17-Jun-2024
  • (2024)Balanced Transformer for Highly Imbalanced Baggage Threat Recognition2024 Advances in Science and Engineering Technology International Conferences (ASET)10.1109/ASET60340.2024.10708761(1-5)Online publication date: 3-Jun-2024
  • (2024)Multi-Scale Hierarchical Contour Framework for Detecting Cluttered Threats in Baggage SecurityIEEE Access10.1109/ACCESS.2024.340772012(77454-77467)Online publication date: 2024
  • (2024)Anomaly Detection in Smart Environments: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2024.339505112(64006-64049)Online publication date: 2024
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