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Tracking a moving source based on a computer vision system: Improving detection using data correlation

Document Type : Research Paper

Authors

Radiation Application Research School, Nuclear Science and Technology Research Institute, AEOI, P. O. Box 11365-3486, Tehran - Iran

Abstract

Data fusion between different sensors can improve the detection of nuclear threats by extracting more reliable and effective information. In this study, tracking a moving radioactive hotspot source using a combination of a radioactive detector (NaI) and a surveillance camera is addressed. For this purpose, three mobile robots were used, and a radioactive source was placed on one of these robots. An algorithm was developed to correlate the radioactive and camera data, so the robot with the highest correlation was selected as the moving source quickly. By increasing the acquisition time from 5 to 125 seconds, the algorithm's success rate in detecting the moving radioactive source increases from 42.7% to 98.3%. In addition, the moving source's detection speed and the detection's precision over different times were studied. The results have presented a model that can be scaled up by equipping surveillance cameras with radioactive detectors to provide a network, and this network can continuously monitor and control a vast area or even a city to detect and track suspicious items.

Highlights

  1. E.R. Davies, Computer and machine vision: theory, algorithms, practicalities, (Academic Press, London, 2012).

 

  1. C. Steger, M. Ulrich, C. Wiedemann, Machine vision algorithms and applications, (John Wiley & Sons, New York, 2018).

 

  1. C.Y. Huang, J.H. Hong, E. Huang, Developing a machine vision inspection system for electronics failure analysis, IEEE Trans. Compon. Packag. Manuf. Technol, 9, 1912 (2019).

 

  1. K.D. Joshi, V. Chauhan, B. Surgenor, June, in: Proceedings of the Canadian Society for Mechanical Engineering International Congress, (CSME, Canada), 26-29 (2016).

 

  1. M. Payal, et al, In: AI and IoT‐Based Intelligent Automation in Robotics Robotics, Edited by A.K. Dubey, A. Kumar, S.K. Rakesh, N. Gayathri, D. Prasenjit, 109-128 (Wiley, 2021).

 

  1. Y. Shen, W. Zhu, Medical image processing using a machine vision-based approach, IJSIP, 6,139 (2013).

 

  1. R. Jain, R. Kasturi, B.G. Schunck, Machine vision, (McGraw-hill, New York, 1995).

 

  1. B.L. Luk, et al., Walking and climbing service robots for safety inspection of nuclear reactor pressure vessels, Meas. Control., 39, 43 (2006).

 

  1. S.J. Schmugge, et al., In: IEEE Winter Conference on Applications of Computer Vision, (IEEE, USA), 1-7 (2016).

 

  1. N. Marturi, et al., In: International Conference on Robotics and Automation for Humanitarian Applications, (IEEE, India), 1-8 (2016).

 

  1. A.R. Benson, et al., The gamma-ray imaging framewor, IEEE Trans. Nucl. Sci., 60, 528 (2013).

 

  1. A. Shaukat, et al., Visual classification of waste material for nuclear decommissioning, Rob. Auton. Sys., 75, 365 (2016).

 

  1. R.M. Vázquez, E. Gutiérrez, In: International Conference on Mechatronics, Electronics and Automotive Engineering, (IEEE, Cuernavaca, Mexico), 175-180 (2018).

 

  1. Y. Zhangfa, et al., Nuclear radiation detection based on uncovered CMOS camera under dynamic scene, Nucl. Instrum. Methods Phys. Res., A. 11, 956 (2020).

 

  1. R. Vilalta, et al., In: International Joint Conference on Artificial Intelligence, Workshop on AI in Space: Intelligence Beyond Planet Earth, (Acta Futura, Netherlands), 111-119 (2012).

 

  1. Par-systems-supports-development-smart-robots-next-generation-nuclear-reactors-southern-research-under-department-energy-grant, https://www.par.com.

 

  1. R. Seulin, et al., In: International Society for Optics and Photonics, (SPIE, California), 128-136 (2004).

 

  1. Augmented reality for testing nuclear components, https://www.eurekalert.org/news-releases/929421.

 

  1. M.L.R. Kelly, Nuclear fuel pellet inspection using machine vision and artificial neural networks, University of Missouri—Rolla, (1995).

 

  1. M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis, and machine vision, (Cengage Learning, 2014).

 

  1. Using Machine Vision, https://www.proquest.com.

 

  1. B.E. Cazalas, Defending Cities Against Nuclear Terrorism: Analysis of A Radiation Detector Network for Ground Based Traffic, Homel. Secur. Aff, 14, 1 (2018).

 

  1. K. Stadnikia, et al., Data fusion for a vision-aided radiological detection system: Correlation methods for single source tracking, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., 954, 161913 (2020).

 

  1. K. Stadnikia, et al., Data fusion for a vision-aided radiological detection system: Calibration algorithm performance, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., 890, 8 (2018).

 

  1. N. Van Thai, L.C. Chen, In: IEEE/ASME Int. Conf. Adv. Intell. Mechatronics Mechatronics, (IEEE, Australia), 1404-1409 (2013).

 

  1. H. Ardiny, S. Witwicki, F. Mondada, Autonomous exploration for radioactive hotspots localization taking account of sensor limitations, Sensors, 19, 292 (2019).

 

  1. A.F. Alwars, A. Farzanehpoor, F, Rahmani, Conceptual design of an orphan gamma source finder, Nucl. Instrum. Methods Phys. Res. A: Accel. Spectrom. Detect. Assoc. Equip., 922, 235 (2019).

 

  1. A.F. Alwars, A. Farzanehpoor, Faezeh Rahmani, A feasibility study of gamma ray source finder development for multiple sources scenario based on a Monte Carlo simulation, Sci. Rep., 11, 1 (2021).

 

  1. F. Mondada, et al., Bringing Robotics to Formal Education: The Thymio Open-Source Hardware Robot, IEEE Robot. Autom. Mag., 24, 77 (2017).

 

  1. K. Eckerman, Endo, Nuclear decay data for dosimetric calculations, Annals of the ICRP, 38, 7 (2008).

 

  1. Liu, Yong-kuo, et al., Path-planning research in radioactive environment based on particle swarm algorithm, Progress in Nuclear Energy, 74, 184 (2014).

 

  1. D. Li, et al, Experiment on gamma-ray generation and application, Nucl. Instrum. Methods Phys. Res. A: Accel. Spectrom. Detect. Assoc. Equip., 1 (2004).

 

  1. R.B. Wilkerson, M.S. thesis, The university of Tennessee, Knoxville, (2016).

 

  1. P. Tandon, et al., Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers, Inf. Syst., 57, 195 (2016).

 

  1. R. Barnowski, et al., Scene data fusion: Real-time standoff volumetric gamma-ray imaging, Nucl. Instrum, 65 (2015).

Keywords

References
  1. E.R. Davies, Computer and machine vision: theory, algorithms, practicalities, (Academic Press, London, 2012).

 

  1. C. Steger, M. Ulrich, C. Wiedemann, Machine vision algorithms and applications, (John Wiley & Sons, New York, 2018).

 

  1. C.Y. Huang, J.H. Hong, E. Huang, Developing a machine vision inspection system for electronics failure analysis, IEEE Trans. Compon. Packag. Manuf. Technol, 9, 1912 (2019).

 

  1. K.D. Joshi, V. Chauhan, B. Surgenor, June, in: Proceedings of the Canadian Society for Mechanical Engineering International Congress, (CSME, Canada), 26-29 (2016).

 

  1. M. Payal, et al, In: AI and IoT‐Based Intelligent Automation in Robotics Robotics, Edited by A.K. Dubey, A. Kumar, S.K. Rakesh, N. Gayathri, D. Prasenjit, 109-128 (Wiley, 2021).

 

  1. Y. Shen, W. Zhu, Medical image processing using a machine vision-based approach, IJSIP, 6,139 (2013).

 

  1. R. Jain, R. Kasturi, B.G. Schunck, Machine vision, (McGraw-hill, New York, 1995).

 

  1. B.L. Luk, et al., Walking and climbing service robots for safety inspection of nuclear reactor pressure vessels, Meas. Control., 39, 43 (2006).

 

  1. S.J. Schmugge, et al., In: IEEE Winter Conference on Applications of Computer Vision, (IEEE, USA), 1-7 (2016).

 

  1. N. Marturi, et al., In: International Conference on Robotics and Automation for Humanitarian Applications, (IEEE, India), 1-8 (2016).

 

  1. A.R. Benson, et al., The gamma-ray imaging framewor, IEEE Trans. Nucl. Sci., 60, 528 (2013).

 

  1. A. Shaukat, et al., Visual classification of waste material for nuclear decommissioning, Rob. Auton. Sys., 75, 365 (2016).

 

  1. R.M. Vázquez, E. Gutiérrez, In: International Conference on Mechatronics, Electronics and Automotive Engineering, (IEEE, Cuernavaca, Mexico), 175-180 (2018).

 

  1. Y. Zhangfa, et al., Nuclear radiation detection based on uncovered CMOS camera under dynamic scene, Nucl. Instrum. Methods Phys. Res., A. 11, 956 (2020).

 

  1. R. Vilalta, et al., In: International Joint Conference on Artificial Intelligence, Workshop on AI in Space: Intelligence Beyond Planet Earth, (Acta Futura, Netherlands), 111-119 (2012).

 

  1. Par-systems-supports-development-smart-robots-next-generation-nuclear-reactors-southern-research-under-department-energy-grant, https://www.par.com.

 

  1. R. Seulin, et al., In: International Society for Optics and Photonics, (SPIE, California), 128-136 (2004).

 

  1. Augmented reality for testing nuclear components, https://www.eurekalert.org/news-releases/929421.

 

  1. M.L.R. Kelly, Nuclear fuel pellet inspection using machine vision and artificial neural networks, University of Missouri—Rolla, (1995).

 

  1. M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis, and machine vision, (Cengage Learning, 2014).

 

  1. Using Machine Vision, https://www.proquest.com.

 

  1. B.E. Cazalas, Defending Cities Against Nuclear Terrorism: Analysis of A Radiation Detector Network for Ground Based Traffic, Homel. Secur. Aff, 14, 1 (2018).

 

  1. K. Stadnikia, et al., Data fusion for a vision-aided radiological detection system: Correlation methods for single source tracking, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., 954, 161913 (2020).

 

  1. K. Stadnikia, et al., Data fusion for a vision-aided radiological detection system: Calibration algorithm performance, Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., 890, 8 (2018).

 

  1. N. Van Thai, L.C. Chen, In: IEEE/ASME Int. Conf. Adv. Intell. Mechatronics Mechatronics, (IEEE, Australia), 1404-1409 (2013).

 

  1. H. Ardiny, S. Witwicki, F. Mondada, Autonomous exploration for radioactive hotspots localization taking account of sensor limitations, Sensors, 19, 292 (2019).

 

  1. A.F. Alwars, A. Farzanehpoor, F, Rahmani, Conceptual design of an orphan gamma source finder, Nucl. Instrum. Methods Phys. Res. A: Accel. Spectrom. Detect. Assoc. Equip., 922, 235 (2019).

 

  1. A.F. Alwars, A. Farzanehpoor, Faezeh Rahmani, A feasibility study of gamma ray source finder development for multiple sources scenario based on a Monte Carlo simulation, Sci. Rep., 11, 1 (2021).

 

  1. F. Mondada, et al., Bringing Robotics to Formal Education: The Thymio Open-Source Hardware Robot, IEEE Robot. Autom. Mag., 24, 77 (2017).

 

  1. K. Eckerman, Endo, Nuclear decay data for dosimetric calculations, Annals of the ICRP, 38, 7 (2008).

 

  1. Liu, Yong-kuo, et al., Path-planning research in radioactive environment based on particle swarm algorithm, Progress in Nuclear Energy, 74, 184 (2014).

 

  1. D. Li, et al, Experiment on gamma-ray generation and application, Nucl. Instrum. Methods Phys. Res. A: Accel. Spectrom. Detect. Assoc. Equip., 1 (2004).

 

  1. R.B. Wilkerson, M.S. thesis, The university of Tennessee, Knoxville, (2016).

 

  1. P. Tandon, et al., Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers, Inf. Syst., 57, 195 (2016).

 

  1. R. Barnowski, et al., Scene data fusion: Real-time standoff volumetric gamma-ray imaging, Nucl. Instrum, 65 (2015).
Journal of Nuclear Science, Engineering and Technology (JONSAT)
Volume 44, Issue 1 - Serial Number 103
April 2023
Pages 67-77
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