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| تعداد مقالات | 9,526 |
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توسعه الگوریتم‌های بینایی ماشین برای شناسایی اهداف آلوده پرتوزا در سناریوهای پرتوی دینامیک | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 79، دی 1403، صفحه 281-292 اصل مقاله (1.14 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.33125.2612 | ||
| نویسندگان | ||
| امیرمحمد بیگ زاده* ؛ هادی اردینی | ||
| پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، تهران، ایران | ||
| تاریخ دریافت: 09 بهمن 1402، تاریخ بازنگری: 21 فروردین 1403، تاریخ پذیرش: 09 اردیبهشت 1403 | ||
| چکیده | ||
| شناسایی و نظارت بر آلودگی رادیواکتیو برای اطمینان از ایمنی عمومی و حفاظت از محیط زیست بسیار مهم است. با این حال، شناسایی منابع رادیواکتیو خارج از کنترل در مکانهای شلوغ می‌تواند چالشبرانگیز باشد، به طور مثال بین مسافران یا وسایل نقلیه. این مطالعه یک رویکرد جدید مبتنی بر ترکیب داده‌ها و روش‌های بینایی ماشین برای تشخیص آلودگی پرتوی در میان اجسام متحرک مشابه معرفی می‌کند. در ابتدا، یک الگوریتم حرکتی برای تعریف 5 جسم متحرک با شکل و اندازه یکسان در یک صفحه دو بعدی، با الهام از معادلات حرکت یک ربات چرخدار کوچک استفاده میشود. این اجسام با سرعت ثابتی در صفحه دو بعدی حرکت می‌کنند. متعاقبا، الگوریتم دیگری با استفاده از روش KLT برای استخراج ویژگی‌های مرتبط و ردیابی همان اشیاء از داده‌های تصویر استفاده میشود. با ترکیب داده های سیستم تشخیص پرتو و بینایی ماشین، این الگوریتم یک یا چند هدف آلوده را شناسایی و شی متحرک آلوده را با موفقیت شناسایی می‌کند. این تحقیق یک روش امیدوارکننده را برای افزایش نظارت بر محیط‌های پرتوی ارائه می‌کند و ادغام تصاویر دوربین‌های نظارتی و سیستم‌های تشخیص پرتوی را برای مناطق عمومی و بزرگ پیشنهاد می‌کند. | ||
| کلیدواژه‌ها | ||
| سناریوی پرتوی؛ آشکارسازی آلودگی رادیواکتیو؛ بینایی ماشین؛ همجوشی داده‌ها؛ مهندسی هسته‌ای؛ دوربین‌های نظارتی | ||
| عنوان مقاله [English] | ||
| Development of Machine Vision Algorithms for Radioactive Contaminated Targets Detection in Dynamic Radiation Scenarios | ||
| نویسندگان [English] | ||
| Amir Mohammad Beigzadeh؛ Hadi Ardiny | ||
| Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran | ||
| چکیده [English] | ||
| Detecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is based on data fusion and machine vision methods. The approach detects radiological contamination among similar moving objects. At first, we use the motion algorithm to define 5 moving objects. They are of the same shape and size and in a two-dimensional plane. Their motion equations were inspired by the small wheeled robot. These objects move with the same speed in the plane. Next, with another algorithm based on the KLT method, we extracted related features and tracked the same objects from the image data. The algorithm combines the beam detection system's data and machine vision. It finds one or more infected targets. It successfully detects the infected moving object. This research shows a promising approach to improve monitoring of radiation environments. It suggests integrating surveillance camera images and radiation detection systems for public and large areas. | ||
| کلیدواژه‌ها [English] | ||
| Radiation scenario, Radioactive contamination detection, Machine vision, Data fusion, Nuclear engineering, Surveillance cameras | ||
| مراجع | ||
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[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 19, no. 11 (2019): 2638. [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, pp. 1-23. Routledge, 2014. [4] M.R. Munawar. "Github." Accessed: Sep. 25, 2023. [Online]. Available: https://github.com/RizwanMunawar/yolov7-object-tracking [5] J.S. Bisht. "Github." Accessed: Sep. 25, 2023. [Online]. Available: https://github.com/jitendrasb24/Car-Detection-OpenCV [6] J. Shi. "Good features to track." In 1994 Proceedings of IEEE conference on computer vision and pattern recognition, pp. 593-600. IEEE, 1994. [7] A. Lukežič, T. Vojíř, L. Čehovin Zajc, J. Matas, and M. Kristan. "Discriminative Correlation Filter Tracker with Channel and Spatial Reliability." Int. J. Comput. Vis 126, no. 7 (2018): 671–688. [8] E.R. Davies. Computer and machine vision: theory, algorithms, practicalities. Academic Press, 2012. [9] C. Steger, M. Ulrich, and C. Wiedemann. Machine vision algorithms and applications. John Wiley & Sons, 2018. [10] C.Y. Huang, J.H. Hong, and E. 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. [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), pp. 26-29. 2016. [12] A.K. Dubey, A. Kumar, S. Rakesh Kumar, N. Gayathri, and P. Das, eds. AI and IoT-based intelligent automation in robotics. John Wiley & Sons, 2021. [13] Y. Shen, and W. 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. [14] R. Jain, R. Kasturi, and B.G. Schunck. Machine vision. Vol. 5. New York: McGraw-hill, 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." Measurement and Control 39, no. 2 (2006): 43-47. [16] S.J. Schmugge, L. Rice, N. Rich Nguyen, J. Lindberg, R. Grizzi, C. Joffe, and M.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. [17] 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." Journal of Environmental Radioactivity 258 (2023): 107105. [18] N. Marturi, A. Rastegarpanah, C. Takahashi, M. Adjigble, R. Stolkin, S. Zurek, M. Kopicki, M. Talha, J.A. Kuo, and Y. 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. [19] A.R. Benson, M.S. Bandstra, D.H. Chivers, T. Aucott, B. Augarten, C. Bates, A. Midvidy et al. "The gamma-ray imaging framework." IEEE Transactions on Nuclear Science 60, no. 2 (2013): 528-532.. [20] Z. Yan, Q. Wei, G. Huang, Y. Hu, Z. Zhang, and T. Dai. "Nuclear radiation detection based on uncovered CMOS camera under dynamic scene." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 956 (2020): 163383. [21] R. Vilalta, S. Kuchibotla, F. Ocegueda-Hernandez, S. Hoang, and L. Pinsky. "Machine learning for identification of sources of ionizing radiation during space missions." In International Joint Conference on Artificial Intelligence, Workshop on AI in Space: Intelligence Beyond Planet Earth. 2011. [22] A. Abdelhakim. "Machine learning for localization of radioactive sources via a distributed sensor network." Soft Computing 27, no. 15 (2023): 10493-10508. [23] J. Huo, X. Hu, J. Wang, and L. Hu. "ACA: Automatic search strategy for radioactive source." Nuclear Engineering and Technology 55, no. 8 (2023): 3030-3038. [24] R.J. Cooper, 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. [25] D. Osthus, P. Mendoza, P. Lalor, E. Casleton, D. Archer, J. Ghawaly, I. Garishvili, A.J. Rowe, I.R. Stewart, and M. 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. [26] E. Cazalas. "Defending cities against nuclear terrorism: Analysis of a radiation detector network for ground based traffic." Homeland Security Affairs 14 (2018). [27] K. Stadnikia, K. Henderson, S. Koppal, and A. Enqvist. "Data fusion for a vision-aided radiological detection system: Correlation methods for single source tracking." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 954 (2020): 161913. [28] L.S. Waters, G.W. McKinney, J.W. Durkee, M.L. Fensin, J.S. Hendricks, M.R. James, R.C. Johns, and D.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. [29] 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, vol. 2, pp. 674-679. 1981. [30] C. Tomasi, and T. Kanade. "Detection and tracking of point." Int J Comput Vis 9, no. 137-154 (1991): 3.
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آمار تعداد مشاهده مقاله: 252 تعداد دریافت فایل اصل مقاله: 207 |
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