Detecting, identifying, and localizing radiological material in urban environments using scan statistics

Abstract

A method is proposed, based on scan statistics, to detect, identify, and localize illicit radiological material using mobile sensors in an urban environment. Our method handles varying levels of background radiation that change according to an (unknown) environment. Our method can accurately determine if a source is present along a street segment as well as identify which of six possible sources generated the radiation. Our method can also localize the source, when detected, to within a few seconds. We have presented our results across a range of decision thresholds allowing stakeholders to evaluate the performance at different false alarm rates. Due to the simplicity of our approach, our models can be trained in a few minutes with very little training data and holds the potential to score a run in real-time. Our method was one of the top performing submissions in the Detecting Radiological Threats in Urban Areas competition.

Current and Prospective Radiation Detection Systems, Screening Infrastructure and Interpretive Algorithms for the Non-Intrusive Screening of Shipping Container Cargo: A Review

“…Interest in this research area is demonstrated by the Detecting Radiological Threats in Urban Environments challenge set in 2019 by the US National Nuclear Security Administration (NNSA), with a prize pool of $100,000 [99]. Using Monte Carlo methods, the NNSA simulated a single 5.08 × 10.16 × 40.64 cm NaI(Tl) detector moving through model urban environments containing different sources such as HEU, WGPu, 131 I, 60 Co or 99m Tc, asking research teams to design detection algorithms to identify the presence of sources, identify the isotopes and locate the point of closest approach of the detector [99,100]. A further demonstration of the interest in mobile detector networks is the USA Defence Advanced Research Projects Agency's (DARPA) SIGMA project in which over 1000 portable radiation detectors were used to map an urban area in Washington, D.C. [101,102].…”

Paper biological risk detection through deep learning and fuzzy system

“…Technological tools are nowadays a means for the automatic detection of biohazards [1], [2] as well as for the analysis of waste such as plastic paper or metal that may involve the same type of risk [3]. Identifying, locating, and detecting biohazards is of utmost importance to safeguard human lives [4]. Among these identification methods, those based on deep learning stand out, for example, employed in [5] to detect plastic waste in rivers and in [6] to develop an intelligent waste management system, both based on deep learning through convolutional networks (CNN) [7], which extends to the detection of biological risks [8].…”