Autonomous Localization of X-Ray Baggage Threats via Weakly Supervised Learning

Divya Velayudhan, Abdelfatah Ahmed, Taimur Hassan, Neha Gour, Muhammad Owais, Mohammed Bennamoun, Ernesto Damiani, Naoufel Werghi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Autonomous X-ray baggage security screening has shown significant strides recently, proving itself a viable solution to the flaws in manual screening, thanks to advancements in deep learning. However, these data-hungry techniques feed on extensively annotated data involving strenuous labor, impeding their advances in baggage screening. Consequently, we present a context-aware transformer for weakly supervised localization to relieve the annotation burden and provide visual interpretability that aids screeners in threat recognition and researchers in identifying the pitfalls of existing systems. The proposed approach can generalize and localize different types of contraband with only cost-effective binary labels without explicit training on item detection. Context extraction block, integrated into the dual-token framework, generates threat-aware context maps, while the token scoring block focuses on minimizing partial activations. Experimental results surpass state of the art (SOTA) methods in terms of classification and localization accuracies. Furthermore, we analyze failures to determine current vulnerabilities and provide new insights for future research.

Original languageBritish English
Article number10401259
Pages (from-to)6563-6572
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number4
StatePublished - 1 Apr 2024


  • Aviation
  • machine learning
  • threat identification
  • threat localization
  • X-ray baggage security


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