ردیابی چشمه متحرک مبتنی بر سیستم بینایی ماشین: بهبود تشخیص با بهره‌گیری از همبستگی داده‌ها

اردینی, هادی; بیگ زاده, امیرمحمد; عسکری, مجتبی

doi:10.24200/nst.2023.1365

نوع مقاله : مقاله پژوهشی

نویسندگان

پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، سازمان انرژی اتمی ایران، صندوق پستی: 3486-11365، تهران ـ ایران

https://doi.org/10.24200/nst.2023.1365

چکیده

ادغام داده‌ها بین حسگرهای مختلف می‌تواند موجب استخراج اطلاعات با دقت و با کیفیت بالاتر گردد و بهبود تشخیص تهدیدهای هسته‌ای را به همراه داشته باشد. در این تحقیق، ردیابی چشمه متحرک با استفاده از تلفیق دادههای سیستم آشکارساز پرتوی و دوربین نظارتی مورد مطالعه قرار گرفت است. بدین منظور الگوریتمی جهت تلفیق و ایجاد همبستگی بین تصاویر دریافتی توسط دوربین و شمارش آشکارساز طراحی شده است تا مسیر حرکت جسمی که بیشترین همبستگی را با اطلاعات شمارش ثبت شده پرتوی توسط سیستم آشکارسازی را دارد به عنوان چشمه متحرک انتخاب نماید. جهت انجام این آزمایش از سه ربات متحرک استفاده شده است که بر روی یکی از این رباتها چشمه پرتوزا به عنوان عامل آلوده به ماده رادیواکتیو قرار گرفته است. با استخراج مسیر حرکت رباتها با کمک تصاویر دوربین و تلفیق آن با اطلاعات شمارش ثبت شده در آشکارساز، الگوریتم مورد نظر توانست در مدت زمان کوتاهی ربات حامل چشمه رادیواکتیو را شناسایی کند. در این تحقیق سرعت تشخیص چشمه متحرک و صحت تشخیص در زمان‌های متفاوت مورد مطالعه قرار گرفت. با افزایش زمان داده‌برداری از 5 ثانیه به 125 ثانیه درصد موفقیت الگوریتم در موقعیت‌یابی چشمه رادیواکتیو متحرک از 7_/42 درصد به 3_/98 درصد افزایش یافت. نتایج این تحقیق نشان داد که با تجهیز دوربینهای شهری با آشکارسازهای هستهای میتوان شبکه‌ای سنسوری ایجاد نمود که یک منطقه و یا شهر را بی‌وقفه پایش و کنترل نمود.

کلیدواژه‌ها

عنوان مقاله [English]

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

نویسندگان [English]

H. Ardiny
A.M. Beigzadeh
M. Askari

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

چکیده [English]

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.

کلیدواژه‌ها [English]

Data fusion
Computer vision
Moving source
Correlation
Radioactive material out of regulatory control

مراجع

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

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

3. 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).

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

5. 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).

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

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

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

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

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

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

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

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

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

15. 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).

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

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

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

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

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

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

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

23. 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).

24. 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).

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

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

27. 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).

28. 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).

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

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

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

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

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

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

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

مجله علوم و فنون هسته ای

ردیابی چشمه متحرک مبتنی بر سیستم بینایی ماشین: بهبود تشخیص با بهره‌گیری از همبستگی داده‌ها

مراجع

مراجع

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

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

3. 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).

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

5. 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).

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

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

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

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

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

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

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

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

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

15. 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).

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

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

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

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

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

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

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

23. 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).

24. 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).

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

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

27. 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).

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

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

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

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

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

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

دوره 44، شماره 1 - شماره پیاپی 103
فروردین 1402
صفحه 67-77

ردیابی چشمه متحرک مبتنی بر سیستم بینایی ماشین: بهبود تشخیص با بهره‌گیری از همبستگی داده‌ها

مراجع

مراجع

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

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

3. 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).

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

5. 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).

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

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

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

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

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

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

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

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

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

15. 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).

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

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

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

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

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

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

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

23. 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).

24. 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).

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

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

27. 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).

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

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

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

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

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

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

دوره 44، شماره 1 - شماره پیاپی 103فروردین 1402صفحه 67-77

دوره 44، شماره 1 - شماره پیاپی 103
فروردین 1402
صفحه 67-77