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Publication Detail
Tackling the X-ray cargo inspection challenge using machine learning
  • Publication Type:
    Conference
  • Authors:
    Jaccard N, Rogers TW, Morton EJ, Griffin LD
  • Publisher:
    SPIE
  • Publication date:
    12/05/2016
  • Published proceedings:
    Anomaly Detection and Imaging with X-Rays (ADIX)
  • Name of conference:
    Anomaly Detection and Imaging with X-Rays (ADIX)
  • Conference place:
    Baltimore, US
  • Conference start date:
    17/04/2016
  • Conference finish date:
    21/04/2016
Abstract
The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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