КОНЦЕПТ ПРОГРАММНО-АППАРАТНОГО КОМПЛЕКСА ДЛЯ РАННЕГО ОБНАРУЖЕНИЯ ЛЕСНЫХ ПОЖАРОВ
Аннотация
В данной статье описывается концептуальное решение программно-аппаратного комплекса для раннего обнаружения лесных пожаров на основе Machine Vision и алгоритмов обработки изображений. Актуальность данной работы представляет собой сложную ситуацию в области борьбы с такими техногенными катастрофами как лесные пожары. Эта статья основана на исследовании уже существующих платформ и систем. Проблема лесных пожаров является одной из наиболее значительных проблем человеческой расы, связанной с проблемами экологии, политики и экономики. Одним из наиболее эффективных методов борьбы с пожарами является их раннее обнаружение и прекращение на начальных этапах, пока пожар не приобрел стихийный характер.
Список литературы
1. Yamagishi H., Yamaguchi J., 1999. “Fire Flame Detection Algorithm Using a Color Camera,” Proceedings of 1999 International Symposium on Micromechatronics and Human Science, Nagoya, Japan, pp. 255-260.
2. Yamagishi H., Yamaguchi J., “A Contour Fluctuation data Processing Method for Fire Flame Detection Using a Color Camera”, IEEE 26th Annual Conference on IECON of the Industrial Electronics Society, Nagoya, Japan, pp. 824.
3. Celik T., Demirel H., Ozkaramanli H., 2006. “Automatic Fire Detection in Video Sequences”, Proceedings of European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September.
4. Chen T., Wu, P., Chiou Y., 2004. “An Early Fire-Detection Method Based on Image Processing,” Proceedings of IEEE ICIP ’04, pp. 1707–1710.
5. Liu C.B., Ahuja N., 2004. “Vision Based Fire Detection,” In: Proceedings of ICPR 2004. Proceedings of the 17th International Conference on. 4, pp.
6. Yuan F.N.,, Liao G.X., Zhang Y.M., et al., 2006. Feature Extraction for Computer Vision Based Fire Detection, Journal of University of Science and Technology of China 36, p. 39.
7. Ugur T.B., Dedeoglu Y., et al., 2006. Computer Vision Based Method for Real-time Fire and Flame Detection, Pattern Recognition Letters 27, p.49.
8. Dedeoglu N., Toreyin B.U., et al., 2005. “Real-time Fire and Flame Detection in Video,” Proceedings of IEEE 30th International Conference on Acoustics, Speech, and Signal Processing (ICASSP’05). Philadelphia, PA, USA. 2005.2(2), pp. 669-672.
9. Xiong Z.Y., Caballero R., Wang H.C., Alan M., Finn Muhidin A.L., Peng, P.Y., 2007. Video-based Smoke Detection: Possibilities, Techniques, and Challenges, IFPA, Fire Suppression and Detection Research and Applications - A Technical Working Conference (SUPDET), Orlando, FL.
10. Yuan F.N., 2008. A Fast Accumulative Motion Orientation Model Based on Integral Image for Video Smoke Detection, Pattern Recognition Letters 29, p. 925.
11. Ugur T.B., Dedeoglu Y., Cetin A.E., 2005. “Wavelet Based Real-time Smoke Detection in Video,” 13th European Signal Process Conf. EUSIPCO2005, Antalya, Turkey.
12. Ho C.C., 2009. Machine Vision Based Real-time Early Flame and Smoke Detection. Measurement Science and Technology 20, No.4.
13. Celik T., Demirel H., Ozkaramanli H., 2006. “Automatic Fire Detection in Video Sequences,” Proceedings of European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 2006.
14. Collins R., Lipton A., Kanade T., et al., 2000. A System for Video Surveillance and Monitoring. Tech. Report CMU-R1-TR-00-12, Robotics Institute, Carnegie Mellon University, 2000.
15. Yu C.Y., Zhang Y.M., 2009. “Texture Analysis of Smoke for Real-time Fire Detection,” IEEE Second International Workshop on Computer Science and Engineering. 2009, Qingdao.
16. Barron J.L., Fleet D.J., Beauchemin S., 1994. Performance of Optical Flow Techniques, International Journal of Computer Vision 12, p. 43.
17. Richard P., Lippmann, 1987. An Introduction to Computing with Neural Network, IEEE ASSP Magazine, p. 4.
18. von Wahl N., Heinen S., Tobera R., Nüßler D., Brauns R., Schröder M., Knott P., Krüll W., and Willms I., 2009. Intermediate Report Internationale Waldbrandbekämpfung iWBB, FHR- Report Nr. 134, FGAN Research Institute for High Frequency Physics and Radar Techniques, Wachtberg, Germany.
19. Henrichs M., 2009. Armored and Tracked Vehicle for Rescue/Extinguish/Defend Missions, 14th International Conference on Automatic Fire Detection, AUBE '09, Duisburg, Germany.
20. AirRobot GmbH & Co. KG, http://www.airrobot.de
21. GTE Industrieelektronik GmbH, http://www.adicos.de
22. Krüll W., Willms I., Tobera R., and Wiggerich B., 2009. Early forest fire detection and suppression - an integrated approach, 14th International Conference on Automatic Fire Detection, AUBE '09, Duisburg, Germany.
23. Tobera R., Krüll W., and Willms I., 2009. Optical smoke and gas sensors as an additional method for early wildfire verification, 14th International Conference on Automatic Fire Detection, AUBE '09, Duisburg, Germany.
24. Hekatron Vertriebs GmbH, http://www.hekatron.de
25. Krüll W., Tobera R., Willms I., von Wahl N., Heinen S., 2008. An integrated approach for early forest fire detection and suppression, First International Conference on Remote Sensing Techniques in Disaster Management and Emergency Response in the Mediterranean Region, Zadar, Croatia.
26. von Wahl N., Heinen S., 2009. Advantages of millimeter waves in fire detection and monitoring, 14th International Conference on Automatic Fire Detection, AUBE '09, Duisburg, Germany.
27. Nüßler D., Essen H., von Wahl N., Zimmermann R. Rötzel S., and Willms I., 2008. Millimeter Wave Propagation through dust, SPIE conference proceedings.
28. Chunyu Yu, Zhibin Mei, Xi Zhang, 2013. A real-time video fire flame and smoke detection algorithm, The 9th Asia-Oceania Symposium on Fire Science and Technology
29. I.A. Csiszar, J.T. Morisette and L. Giglio, "Validation of active fire detection from moderate-resolution satellite sensors: the MODIS example in northern Eurasia," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, pp. 1757-1764, 2006.
30. W. Schroeder, P. Oliva, L. Giglio and I.A. Csiszar, "The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment," Remote Sensing of Environment, vol. 143, pp. 85-96, 2014.
31. C.L. Cover, "Corine Land Cover," European Environment Agency, Copenhagen, 2000.
32. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg and others, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
33. N. Chiaraviglio, T. Artés, R. Bocca, J. López, A. Gentile, J.S. M. Ayanz, A. Cortés and T. Margalef, "Automatic fire perimeter determination using MODIS hotspots information," in e-Science (e-Science), 2016 IEEE 12th International Conference on, 2016.
34. Tomàs Artés, Roberto Boca, Giorgio Liberta, Jesús San-Miguel, "Non-supervised method for early forest fire detection and rapid mapping," Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104440R (6 September 2017); doi: 10.1117/12.2280714
Рецензия
Для цитирования:
Култышев Е.А. КОНЦЕПТ ПРОГРАММНО-АППАРАТНОГО КОМПЛЕКСА ДЛЯ РАННЕГО ОБНАРУЖЕНИЯ ЛЕСНЫХ ПОЖАРОВ. Вестник Казахстанско-Британского технического университета. 2020;17(4):161-170.
For citation:
Kultyshev Y. THE CONCEPT OF A SOFTWARE AND HARDWARE COMPLEX FOR EARLY DETECTION OF FOREST FIRES. Herald of the Kazakh-British technical university. 2020;17(4):161-170.
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