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:: Volume 15, Issue 2 (12-2025) ::
JGST 2025, 15(2): 61-74 Back to browse issues page
Radiation mapping and detection of out-of-control radioactive sources by developing algorithms based on machine vision
AmirMOhammad Beigzadeh * , Hadi Ardiny
Abstract:   (62 Views)
In this paper, we present a novel approach to ray mapping and detection through the development of machine vision algorithms. The primary objective is to enhance the efficiency and accuracy of identifying and locating out-of-control radioactive sources in complex and dynamic environments. Initially, an algorithm was devised based on the movement patterns of Timio's wheeled robots. This algorithm takes into account various factors, such as their random movement within the environment, obstacle and wall detection capabilities, directional adjustments when approaching each other, and other relevant scenarios. To facilitate experimentation, we defined 10 representative characters of these robots on the following page, each occupying a 10 x 10 square meter area. A video capturing their movement was recorded over a duration of 120 seconds at a frame rate of 25 frames per second. The coordinates of their movement paths were then recorded within this time frame. Subsequently, a machine vision algorithm was developed based on the KLT tracking method equations. This algorithm effectively tracked the movement paths of the characters, and the resulting coordinates were compared with the actual movement coordinates for validation. In the next phase, a radiation scenario was introduced by placing a radioactive source on one of the characters. To simulate this, we employed 3000 Monte Carlo codes specifically designed to account for the presence of the source. The output of these codes, recorded as counts in the detector, was extracted and stored for analysis. Finally, to detect the moving radioactive source within an environment containing a high number of characters, algorithms based on data correlation between the movement paths and the recorded counts in the detector were utilized.
 
Article number: 5
Keywords: Radiation mapping, radioactive source tracking, machine vision, nuclear terrorism, radiation scenario.
Full-Text [PDF 1038 kb]   (45 Downloads)    
Type of Study: Research | Subject: Photo&RS
Received: 2024/02/4 | Accepted: 2025/10/5
References
1. H. Al Hamrashdi, S. D. Monk, and D. Cheneler, "Passive Gamma-Ray and Neutron Imaging Systems for National Security and Nuclear Non-Proliferation in Controlled and Uncontrolled Detection Areas: Review of Past and Current Status," Sensors, vol. 19, no. 11. 2019. doi: 10.3390/s19112638. [DOI:10.3390/s19112638]
2. C. Fernandez, "These are the top 10 busiest airports in the world-5 of them are in the U.S." Accessed: Sep. 23, 2023. [Online]. Available: https://www.cnbc.com/2023/04/10/world-busiest-airports-airports-council-international-ranking.html
3. P. Andreas, "A tale of two borders: The US-Canada and US-Mexico lines after 9--11," in The Rebordering of North America, Routledge, 2014, pp. 1-23.
4. J. S. Bisht, "Github." Accessed: Sep. 25, 2023. [Online]. Available: https://github.com/jitendrasb24/Car-Detection-OpenCV
5. J. Shi and Tomasi, "Good features to track," in 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1994, pp. 593-600. doi: 10.1109/CVPR.1994.323794. [DOI:10.1109/CVPR.1994.323794]
6. E. R. Davies, Computer and machine vision: theory, algorithms, practicalities. Academic Press, 2012.
7. C. Steger, M. Ulrich, and C. Wiedemann, Machine vision algorithms and applications. John Wiley & Sons, 2018.
8. S. J. Schmugge et al., "Detection of cracks in nuclear power plant using spatial-temporal grouping of local patches," in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1-7. doi: 10.1109/WACV.2016.7477601. [DOI:10.1109/WACV.2016.7477601]
9. E. Cazalas, "Defending cities against nuclear terrorism: Analysis of a radiation detector network for ground based traffic," Homel. Secur. Aff., vol. 14, 2018.
10. C.-Y. Huang, J.-H. Hong, and E. Huang, "Developing a Machine Vision Inspection System for Electronics Failure Analysis," IEEE Trans. Components, Packag. Manuf. Technol., vol. 9, no. 9, pp. 1912-1925, 2019, doi: 10.1109/TCPMT.2019.2924482. [DOI:10.1109/TCPMT.2019.2924482]
11. K. D. Joshi, V. D. Chauhan, and B. W. Surgenor, "Real time recognition and counting of Indian currency coins using machine vision: a preliminary analysis," in Proceedings of the Canadian Society for Mechanical Engineering International Congress (CSME), 2016, pp. 26-29.
12. A. K. Dubey, A. Kumar, S. R. Kumar, N. Gayathri, and P. Das, AI and IoT-based Intelligent Automation in Robotics. John Wiley & Sons, 2021. [DOI:10.1002/9781119711230]
13. Y. Shen and W. Zhu, "Medical image processing using a machine vision-based approach," Int. J. signal Process. Image Process. Pattern Recognit., vol. 6, no. 3, pp. 139-146, 2013.
14. R. Jain, R. Kasturi, B. G. Schunck, and others, Machine vision, vol. 5. McGraw-hill New York, 1995.
15. B. L. Luk, A. A. Collie, D. S. Cooke, and S. Chen, "Walking and Climbing Service Robots for Safety Inspection of Nuclear Reactor Pressure Vessels," Meas. Control, vol. 39, no. 2, pp. 43-47, Mar. 2006, doi: 10.1177/002029400603900201. [DOI:10.1177/002029400603900201]
16. A. R. Benson et al., "The Gamma-Ray Imaging Framework," IEEE Trans. Nucl. Sci., vol. 60, no. 2, pp. 528-532, 2013, doi: 10.1109/TNS.2013.2245342. [DOI:10.1109/TNS.2013.2245342]
17. N. Marturi et al., "Towards advanced robotic manipulation for nuclear decommissioning: A pilot study on tele-operation and autonomy," in 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), 2016, pp. 1-8. doi: 10.1109/RAHA.2016.7931866. [DOI:10.1109/RAHA.2016.7931866]
18. K. Stadnikia, K. Henderson, S. Koppal, and A. Enqvist, "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., vol. 954, Feb. 2020, doi: 10.1016/j.nima.2019.02.040. [DOI:10.1016/j.nima.2019.02.040]
19. H. Ardiny, A. Beigzadeh, and H. Mahani, "MCNPX simulation and experimental validation of an unmanned aerial radiological system (UARS) for rapid qualitative identification of weak hotspots," J. Environ. Radioact., vol. 258, p. 107105, 2023, doi: https://doi.org/10.1016/j.jenvrad.2022.107105 [DOI:10.1016/j.jenvrad.2022.107105.]
20. D. Osthus et al., "Tracking the location of a road-constrained radioactive source with a network of detectors," Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 1039, p. 166992, Sep. 2022, doi: 10.1016/j.nima.2022.166992. [DOI:10.1016/j.nima.2022.166992]
21. J. Huo, X. Hu, J. Wang, and L. Hu, "ACA: Automatic search strategy for radioactive source," Nucl. Eng. Technol., vol. 55, no. 8, pp. 3030-3038, 2023, doi: https://doi.org/10.1016/j.net.2023.05.017 [DOI:10.1016/j.net.2023.05.017.]
22. R. J. Cooper et al., "Networked Sensing for Radiation Detection, Localization, and Tracking," arXiv Prepr. arXiv2307.13811, 2023.
23. L. S. Waters et al., "The MCNPX Monte Carlo Radiation Transport Code," in AIP Conference Proceedings, AIP, 2007, pp. 81-90. doi: 10.1063/1.2720459. [DOI:10.1063/1.2720459]
24. B. D. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," in IJCAI'81: 7th international joint conference on Artificial intelligence, Vancouver, Canada, Aug. 1981, pp. 674-679. [Online]. Available: https://hal.science/hal-03697340
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Beigzadeh A, Ardiny H. Radiation mapping and detection of out-of-control radioactive sources by developing algorithms based on machine vision. JGST 2025; 15 (2) : 5
URL: http://jgst.issgeac.ir/article-1-1176-en.html


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Volume 15, Issue 2 (12-2025) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology