https://doi.org/10.1140/epjp/s13360-024-04974-x
Regular Article
Exploring radiological contamination among the same moving objects based on a fusion between radioactive detectors and surveillance cameras
Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
Received:
8
January
2024
Accepted:
3
February
2024
Published online:
19
February
2024
The detection and monitoring of radiological contamination are crucial for ensuring public safety and environmental protection. However, identifying sources of radiation among passengers or cars can be challenging. This study proposes a novel approach based on data fusion and machine vision methods for identifying radiological contamination among the same moving objects. The proposed algorithm utilizes the CSRT and KLT methods to extract relevant features and track the same objects from camera data. The algorithm makes a correlation between radiological system data and detected objects to find a contaminated target. The effectiveness of the proposed algorithm was evaluated in a laboratory testbed with small robots, and the results demonstrated high accuracy in detecting radiological contamination. This research demonstrates a promising approach to improving radiation monitoring and emergency response efforts by integrating surveillance cameras and radiological detection systems for public and big areas.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.