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

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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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision tasks

        • Visual inspection

  • Machine learning

    • Machine learning approaches

      • Neural networks