Portable X-ray imaging systems are routinely used by bomb squads throughout the world to image the contents of suspicious packages and explosive devices. The images are used by bomb technicians to determine whether or not packages contain explosive devices or device components. In events of positive detection, the images are also used to understand device design and to devise countermeasures. The quality of the images is considered to be of primary importance by users and manufacturers of these systems, since it affects the ability of the users to analyze the images and to detect potential threats. As such, there exist national standards that set minimum acceptable image-quality levels for the performance of these imaging systems. An implicit assumption is that better image quality leads to better user identification of components in explosive devices and, therefore, better informed plans to render them safe. However, there is no previously published experimental work investigating this. Toward advancing progress in this direction, the authors developed the new NIST-LIVE X-ray improvised explosive device (IED) image-quality database. The database consists of: a set of pristine X-ray images of IEDs and benign objects; a larger set of distorted images of varying quality of the same objects; ground-truth IED component labels for all images; and human task-performance results locating and identifying the IED components. More than 40 trained U.S. bomb technicians were recruited to generate the human task-performance data. They use the database to show that identification probabilities for IED components are strongly correlated with image quality. They also show how the results relate to the image-quality metrics described in the current U.S. national standard for these systems, and how their results can be used to inform the development of baseline performance requirements. They expect these results to directly affect future revisions of the standard.
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