Study of bomb technician threat identification task performance on distorted X-ray images
Jack L. Glover, Praful Gupta, Nicholas G. Paulter Jr., Alan C. Bovik
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 counter measures. 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 international 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 to establish this. Towards advancing progress in this direction, we developed the new NIST-LIVE X-ray 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 US bomb technicians were recruited to generate the human task performance data. We use the database to show that identification probabilities for IED components are strongly correlated with image quality. We also show how the results relate the image quality metrics described in the current international
, Gupta, P.
, Paulter Jr., N.
and Bovik, A.
Study of bomb technician threat identification task performance on distorted X-ray images, Journal of Perceptual Imaging
(Accessed March 25, 2023)