An international collaborative study of cancer risk among workers in the nuclear industry is tinder way to estimate direetly the cancer risk following protracted low-dose exposure to ionising radiation. An essential aspect of this study is the characterisation and quantification of errors in available dose estimates. One major source of errors is dosemeter response in workplace exposure conditions. Little information is available on energy and geometry response for most of the 124 different dosemeters used historically in participating facilities. Experiments were therefore set up to assess this. using 10 dosemeter types representative of those used over time. Results show that the largest errors were associated with the response of early dosemeters to low-energy photon radiation. Good response was found with modern dosemeters. even at low energy. These results are being used to estimate errors in the response for each dosemeter type, used in the participating facilities, so that these can be taken into account in the estimates of cancer risk.
Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of LiDAR and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents a novel object detection, tracking, and source-object attribution analysis that is capable of operating in real-time. By implementing this analysis pipeline on a custom developed system that comprises a static 2 × 4 × 16 inch NaI(Tl) detector co-located with a 64-beam LiDAR and 4 monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time, and use physics-based models to reliably discriminate between source-carrying and non-source-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and Li-DAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment, and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology.
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