1 May 2017 A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery
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Abstract
Previously, we investigated the use of Convolutional Neural Networks (CNNs) to detect so-called Small Metallic Threats (SMTs) hidden amongst legitimate goods inside a cargo container. We trained a CNN from scratch on data produced by a Threat Image Projection (TIP) framework that generates images with realistic variation to robustify performance. The system achieved 90% detection of containers that contained a single SMT, while raising 6% false positives on benign containers. The best CNN architecture used the raw high energy image (single-energy) and its logarithm as input channels. Use of the logarithm improved performance, thus echoing studies on human operator performance. However, it is an unexpected result with CNNs. In this work, we (i) investigate methods to exploit material information captured in dual-energy images, and (ii) introduce a new CNN training scheme that generates ‘spot-the-difference’ benign and threat pairs on-the-fly. To the best of our knowledge, this is the first time that CNNs have been applied directly to raw dual-energy X-ray imagery, in any field. To exploit dual-energy, we experiment with adapting several physics-derived approaches to material discrimination from the cargo literature, and introduce three novel variants. We hypothesise that CNNs can implicitly learn about the material characteristics of objects from the raw dual-energy images, and use this to suppress false positives. The best performing method is able to detect 95% of containers containing a single SMT, while raising 0.4% false positives on benign containers. This is a step change improvement in performance over our prior work
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Thomas W. Rogers, Thomas W. Rogers, Nicolas Jaccard, Nicolas Jaccard, Lewis D. Griffin, Lewis D. Griffin, } "A deep learning framework for the automated inspection of complex dual-energy x-ray cargo imagery", Proc. SPIE 10187, Anomaly Detection and Imaging with X-Rays (ADIX) II, 101870L (1 May 2017); doi: 10.1117/12.2262662; https://doi.org/10.1117/12.2262662
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