Abstract
The University of Florida is taking a multidisciplinary approach to fuse the data between 3D vision sensors and radiological sensors in hopes of creating a system capable of not only detecting the presence of a radiological threat, but also tracking it. The key to developing such a vision-aided radiological detection system, lies in the count rate being inversely dependent on the square of the distance. Presented in this paper are the results of the calibration algorithm used to predict the location of the radiological detectors based on 3D distance from the source to the detector (vision data) and the detectors count rate (radiological data). Also presented are the results of two correlation methods used to explore source tracking.
Highlights
This project combines 3D vision sensors and radiological detectors in hopes to more efficiently and effectively locate potential nuclear threats in dynamic environments such as airports and package and mailing facilities
Special nuclear material (SNM) is a major concern as it can potentially be used in nuclear weapons [1]
The background count rate is taken into account in the calibration results
Summary
This project combines 3D vision sensors and radiological detectors in hopes to more efficiently and effectively locate potential nuclear threats in dynamic environments such as airports and package and mailing facilities. Special nuclear material (SNM) is a major concern as it can potentially be used in nuclear weapons [1]. Current systems such as portal monitors and distributed sensor networks approach this issue [2] [3]. Combining radiological sensors with vision sensors will help to see potential occlusions, suspicious persons or items, and determine if there is a cause for concern. It is possible to combine radiological sensors and vision sensors through data fusion. The concept is based on the radiological count rate being inversely dependent on the square of the distance
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