ردیابی و کشف مواد رادیواکتیو خارج از کنترل در سناریوهای پر ازدحام مبتنی بر همپوشانی تصاویر سامانه بینایی ماشین-نقشه شمارش پرتوی(روش سریع و کارآمد)

نوع مقاله : مقاله پژوهشی

نویسندگان

پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، تهران، ایران

چکیده

گسترش سریع فناوری هسته ای در مقیاس جهانی نگرانی ها را در مورد خطرات قابل توجه مواد رادیواکتیو برای جوامع انسانی و محیط زیست افزایش داده است. در محیط‌هایی که با سطوح بالای ترافیک و ازدحام مشخص می‌شوند، شناسایی و ردیابی مؤثر مواد رادیواکتیو خارج از کنترل برای تضمین ایمنی و امنیت افراد و جوامع بسیار مهم است. این مطالعه یک روش نوآورانه و کارآمد را معرفی می‌کند که تصاویر سیستم بینایی ماشین را با نقشه‌های شمارش پرتوی برای شناسایی و کشف دقیق مواد رادیواکتیو در سناریوهای پرجمعیت ادغام می‌کند. این روش با بهره گیری از فناوری پیشرفته بینایی ماشین در کنار قابلیت‌های آشکارسازهای پرتوی، تشخیص دقیق را تسهیل می کند و پاسخ به موقع به تهدیدات بالقوه را ممکن می سازد. با بر هم نهی تصاویر با نقشه شمارش پرتو، یک سیستم قوی برای نظارت و بازرسی مواد رادیواکتیو خارج از کنترل ایجاد می‌شود. این یافته‌ها نتایج امیدوارکننده‌ای را برای افزایش پروتکل‌های ایمنی در محیط‌های پیچیده نشان می‌دهند، به ویژه بر اهمیت نظارت بر تشعشع و شناسایی سریع مواد رادیواکتیو خارج از کنترل برای حفاظت از سلامت عمومی و محیط‌زیست تأکید می‌کنند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Tracking and Exploring out-of-Control Radioactive Materials in Crowded Scenarios based on the Overlaying of Machine Vision System Images-Radiation Counting Map (Fast and Efficient Method)

نویسندگان [English]

  • Amir Mohammad Beigzadeh
  • Hadi Ardiny
Radiation Application Research School, NSTRI, Tehran, Iran
چکیده [English]

The rapid expansion of nuclear technology on a global scale has heightened concerns regarding the significant risks posed by radioactive materials to both human societies and the environment. In environments characterized by high levels of traffic and congestion, the effective detection and tracking of out-of-control radioactive materials are paramount to ensuring the safety and security of individuals and communities. This study introduces an innovative and efficient method that integrates machine vision system images with radiation-counting maps to precisely identify and explore radioactive materials in densely populated scenarios. Leveraging advanced machine vision technology alongside the capabilities of multi-radiation detectors, this methodology facilitates accurate detection and enables timely responses to potential threats. By aligning image overlay techniques with thorough ray count map analysis, a robust system is established for monitoring and investigating instances of out-of-control radioactive materials. These findings exhibit promising outcomes for enhancing safety protocols in intricate environments, particularly emphasizing the importance of radiation monitoring and the swift identification of out-of-control radioactive materials to safeguard public health and the environment.

کلیدواژه‌ها [English]

  • Machin vision
  • Nuclear threats
  • Radiation map
  • Object Tracking
  • Monte carlo
  • Radioactive source
[1] Lowenthal, Gerhart, and Peter Airey. Practical applications of radioactivity and nuclear radiations. Cambridge university press, 2001.
[2] Adamantiades, Achilles, and Ioannis Kessides. "Nuclear power for sustainable development: current status and future prospects." Energy Policy 37, no. 12 (2009): 5149-5166.
[3] Eisenbud, Merrill, and Thomas F. Gesell. Environmental Radioactivity From Natural, Industrial and Military Sources: from Natural, Industrial And Military Sources. Elsevier, 1997.
[4] Cember, Herman. "Introduction to health physics." (1969): xi+-422.
[5] Alhassani, Nasser Ali Mohamed. "Terrorism and the Threat of Insecure Radioactive Material." (2017).
[6] Johns, Russell E., and Mark Schanfein. "Nuclear Material Accounting and Control." In Nuclear Safeguards, Security, and Nonproliferation, pp. 157-229. Butterworth-Heinemann, 2019.
[7] Ahmad, Muhammad Ikmal, Mohd Hafizi Ab. Rahim, Rosdiadee Nordin, Faizal Mohamed, Asma Abu-Samah, and Nor Fadzilah Abdullah. "Ionizing radiation monitoring technology at the verge of internet of things." Sensors 21, no. 22 (2021): 7629.
[8] Knoll, Glenn F. Radiation detection and measurement. John Wiley & Sons, 2010.
[9] EURO, POL. "Combating illicit trafficking in nuclear and other radioactive material." IAEA Nuclear Security 6 (2007): 3-12.
[10] Onderco, Michal, and Madeline Zutt. "Emerging technology and nuclear security: What does the wisdom of the crowd tell us?." Contemporary Security Policy 42, no. 3 (2021): 286-311.
[11] Seco, Joao, Ben Clasie, and Mike Partridge. "Review on the characteristics of radiation detectors for dosimetry and imaging." Physics in Medicine & Biology 59, no. 20 (2014): R303.
[12] Vetter, Kai. "Multi-sensor radiation detection, imaging, and fusion." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 805 (2016): 127-134.
[13] Golnabi, Hossein, and A. Asadpour. "Design and application of industrial machine vision systems." Robotics and Computer-Integrated Manufacturing 23, no. 6 (2007): 630-637.
[14] Bandstra, Mark S, Tim Aucott, Daniel H. Chivers, James Siegrist, and Kai Vetter. "The machine vision radiation detection system." In 2011 IEEE Nuclear Science Symposium Conference Record, pp. 326-330. IEEE, 2011.
[15] Morgan, Dane, Ghanshyam Pilania, Adrien Couet, Blas P. Uberuaga, Cheng Sun, and Ju Li. "Machine learning in nuclear materials research." Current Opinion in Solid State and Materials Science 26, no. 2 (2022): 100975.
[16] Wang, Shaohui, Ya Hou, Xuanhao Li, Xianli Meng, Yi Zhang, and Xiaobo Wang. "Practical implementation of artificial intelligence-based deep learning and cloud computing on the application of traditional medicine and western medicine in the diagnosis and treatment of rheumatoid arthritis." Frontiers in pharmacology 12 (2021): 765435.
[17] https://learnopencv.com/understanding-multiple-object-tracking-using-deepsort
[18] Tang, Chenwei, Caiyang Yu, Yi Gao, Jianming Chen, Jiaming Yang, Jiuling Lang, Chuan Liu, Ling Zhong, Zhenan He, and Jiancheng Lv. "Deep learning in nuclear industry: A survey." Big Data Mining and Analytics 5, no. 2 (2022): 140-160.
[19] Doyle, James. Nuclear safeguards, security and nonproliferation: achieving security with technology and policy. Elsevier, 2011.
[20] Chierici, Andrea, Salvatore Angelo Cancemi, Ernst Niederleithinger, and Rosa Lo Frano. "Enhanced radioactive waste drum monitoring: A sensorized LoRa-based network for identification and integrity assessment." Nuclear Engineering and Design 424 (2024): 113231.
[21] Skilton, Robert Mark. "Autonomous visual inspection for generic defect detection in nuclear fusion facilities." PhD diss., University of Surrey, 2023.
[22] Bian, Jiang. "Video trajectory analysis." PhD diss., 2019.
[23] Davies, E. Roy. Computer and machine vision: theory, algorithms, practicalities. Academic Press, 2012.
[24] Steger, Carsten, Markus Ulrich, and Christian Wiedemann. Machine vision algorithms and applications. John Wiley & Sons, 2018.
[25] Huang, Chien-Yi, Jyun-Hong Hong, and Eric Huang. "Developing a machine vision inspection system for electronics failure analysis." IEEE Transactions on Components, Packaging and Manufacturing Technology 9, no. 9 (2019): 1912-1925.
[26] Joshi, Keyur D, Vedang D. Chauhan, and Brian 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), pp. 26-29. 2016.
[27] Dubey, Ashutosh Kumar, Abhishek Kumar, S. Rakesh Kumar, N. Gayathri, and Prasenjit Das, eds. AI and IoT-Based Intelligent Automation in Robotics. John Wiley & Sons, 2021.
[28] Shen, Ying, and Weihua Zhu. "Medical image processing using a machine vision-based approach." International Journal of Signal Processing, Image Processing and Pattern Recognition 6, no. 3 (2013): 139-146.
[29] Kosiba, David A, and Rangachar Kasturi. "Machine vision." In Microelectronics, pp. 19-1. CRC Press, 2018.
[30] Luk, B. L, A.A. Collie, D.S. Cooke, and S. Chen. "Walking and climbing service robots for safety inspection of nuclear reactor pressure vessels." Measurement and Control 39, no. 2 (2006): 43-47.
[31] Schmugge, Stephen J, Lance Rice, N. Rich Nguyen, John Lindberg, Robert Grizzi, Chris Joffe, and Min C. Shin. "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), pp. 1-7. IEEE, 2016.
[32] Ardiny, Hadi, Amirmohammad Beigzadeh, and Hojjat Mahani. "MCNPX simulation and experimental validation of an unmanned aerial radiological system (UARS) for rapid qualitative identification of weak hotspots." Journal of Environmental Radioactivity 258 (2023): 107105.
[33] Marturi, Naresh, Alireza Rastegarpanah, Chie Takahashi, Maxime Adjigble, Rustam Stolkin, Sebastian Zurek, Marek Kopicki, Mohammed Talha, Jeffrey A. Kuo, and Yasemin Bekiroglu. "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), pp. 1-8. IEEE, 2016.
[34] Benson, Austin R, Mark S. Bandstra, Daniel H. Chivers, Timothy Aucott, Ben Augarten, Cameron Bates, Adam Midvidy et al. "The gamma-ray imaging framework." IEEE Transactions on Nuclear Science 60, no. 2 (2013): 528-532.
[35] Huo, Jianwen, Xulin Hu, Junling Wang, and Li Hu. "ACA: Automatic search strategy for radioactive source." Nuclear Engineering and Technology 55, no. 8 (2023): 3030-3038.
[36] Cooper, R.J, N. Abgrall, G. Aversano, M.S. Bandstra, D. Hellfeld, T.H. Joshi, V. Negut et al. "Networked sensing for radiation detection, localization, and tracking." In Journal of Physics: Conference Series, vol. 2586, no. 1, p. 012125. IOP Publishing, 2023.
[37] Osthus, Dave, Paul Mendoza, Peter Lalor, Emily Casleton, Dan Archer, James Ghawaly, Irakli Garishvili, Andrew J. Rowe, Ian R. Stewart, and Michael Willis. "Tracking the location of a road-constrained radioactive source with a network of detectors." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1039 (2022): 166992.
[38] Cazalas, Edward. "Defending cities against nuclear terrorism: Analysis of a radiation detector network for ground based traffic." Homeland Security Affairs 14 (2018).
[39] https://gitlab.iti.uni-luebeck.de/minimize-surprise/basic-swarm-behaviors-thymio/-/forks
[40] Tomasi, Carlo, and Takeo Kanade. "Detection and tracking of point." Int J Comput Vis 9, no. 137-154 (1991): 3.
[41] Waters, Laurie S., Gregg W. McKinney, Joe W. Durkee, Michael L. Fensin, John S. Hendricks, Michael R. James, Russell C. Johns, and Denise B. Pelowitz. "The MCNPX Monte Carlo radiation transport code." In AIP Conference Proceedings, vol. 896, no. 1, pp. 81-90. American Institute of Physics, 2007.