Natural Hazards

, Volume 92, Issue 1, pp 511–523 | Cite as

A system to detect potential fires using a thermographic camera

Original Paper
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Abstract

This paper describes a fire monitoring system, based on a thermographic camera, for electrical appliances in interior spaces. These appliances are at particular risk because they are vulnerable to the carelessness of users (46% of electrical appliances fires are caused this way). The system compromises a thermographic camera, rotating on a two-axis robotic arm, controlled by a fire monitoring algorithm that detects the appliances’ status. Once the system’s accuracy and ability to identify the status of each appliance had been tested, the camera’s rotation sequence was planned. To achieve the best efficiency, bearing in mind that fires can break out very quickly, the sequence was based on the distance between monitored appliances. Over a nine-hour period, monitoring six appliances, the proposed method resulted in about 295 (about 7%) more rotations than those produced by a method of arbitrary ordering. This effectiveness increases when more appliances are monitored over greater periods. The system’s main contribution to fire safety is the application and full utilization of the thermal camera, detecting the beginnings of a fire before it can break out.

Keywords

Fire monitoring Thermographic camera Robotic arm Rotation planning 
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Notes

Acknowledgements

The authors express their thanks to Professor Ghang Lee (Department of Architectural Engineering, Yonsei University, South Korea) for his advice and generosity about information offering.

References

  1. Beji T, Verstockt S, Van de Walle R, Merci B (2014) On the use of real-time video to forecast fire growth in enclosures. Fire Technol 50:1021–1040Google Scholar
  2. Çelik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44:147–158CrossRefGoogle Scholar
  3. Çetin AE, Dimitropoulos K, Gouverneur B, Grammalidis N, Günay O, Habiboǧlu YH, Töreyin BU, Verstockt S (2013) Video fire detection—review. Digit Signal Proc 23:1827–1843CrossRefGoogle Scholar
  4. Cheng C, Sun F, Zhou X (2011) One fire detection method using neural networks. Tsinghua Sci Technol 16:31–35CrossRefGoogle Scholar
  5. Du HX, Wu HP, Wang FJ, Yan RZ (2015) The detection of high-strength concrete exposed to high temperatures using infrared thermal imaging technique. Mater Res Innov 19:S1-162–S161-167CrossRefGoogle Scholar
  6. Emmy Prema C, Vinsley SS, Suresh S (2016) Multi feature analysis of smoke in YUV color space for early forest fire detection. Fire Technol 52:1319–1342CrossRefGoogle Scholar
  7. Fischer A, Fischer F, Jäger G, Keilwagen J, Molitor P, Grosse I (2014) Exact algorithms and heuristics for the quadratic traveling salesman problem with an application in bioinformatics. Discrete Appl Math 166:97–114CrossRefGoogle Scholar
  8. Gade R, Moeslund TB (2014) Thermal cameras and applications: a survey. Mach Vis Appl 25:245–262CrossRefGoogle Scholar
  9. Grapinet M, De Souza P, Smal JC, Blosseville JM (2013) Characterization and simulation of optical sensors. Accid Anal Prev 60:344–352CrossRefGoogle Scholar
  10. Gutmacher D, Hoefer U, Wöllenstein J (2012) Gas sensor technologies for fire detection. Sens Actuat B Chem 175:40–45CrossRefGoogle Scholar
  11. Hackner A, Oberpriller H, Ohnesorge A, Hechtenberg V, Müller G (2016) Heterogeneous sensor arrays: merging cameras and gas sensors into innovative fire detection systems. Sens Actuat B Chem 231:497–505CrossRefGoogle Scholar
  12. Han X-F, Jin JS, Wang M-J, Jiang W, Gao L, Xiao L-P (2017) Video fire detection based on gaussian mixture model and multi-color features. Signal Image Video Process 1:1419CrossRefGoogle Scholar
  13. Isaiah P, Shima T (2015) Motion planning algorithms for the dubins travelling salesperson problem. Automatica 53:247–255CrossRefGoogle Scholar
  14. Jelicic V, Magno M, Brunelli D, Paci G, Benini L (2013) Context-Adaptive multimodal wireless sensor network for energy-efficient gas monitoring. Sens J IEEE 13:328–338CrossRefGoogle Scholar
  15. Jia Y, Lin G, Wang J, Fang J, Zhang Y (2016a) Light condition estimation based on video fire detection in spacious buildings. Arab J Sci Eng 41:1031–1041CrossRefGoogle Scholar
  16. Jia Y, Yuan J, Wang J, Fang J, Zhang Q, Zhang Y (2016b) A saliency-based method for early smoke detection in video sequences. Fire Technol 52:1271–1292CrossRefGoogle Scholar
  17. Kanwal K, Liaquat A, Mughal M, Abbasi AR, Aamir M (2016) Towards development of a low cost early fire detection system using wireless sensor network and machine vision. Wirel Pers Commun 95:475CrossRefGoogle Scholar
  18. Karakus C, Gurbuz AC, Tavli B (2013) Analysis of energy efficiency of compressive sensing in wireless sensor networks. Sens J IEEE 13:1999–2008CrossRefGoogle Scholar
  19. Khodayar F, Sojasi S, Maldague X (2016) Infrared thermography and NDT: 2050 horizon. Quant InfraRed Thermogr J 13:210–231CrossRefGoogle Scholar
  20. Ko B, Cheong K-H, Nam J-Y (2010) Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian networks. Fire Saf J 45:262–270CrossRefGoogle Scholar
  21. Kong SG, Jin D, Li S, Kim H (2016) Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Saf J 79:37–43CrossRefGoogle Scholar
  22. Krüger S, Despinasse M-C, Raspe T, Nörthemann K, Moritz W (2017) Early fire detection: Are hydrogen sensors able to detect pyrolysis of house hold materials? Fire Saf J 91:1059–1067CrossRefGoogle Scholar
  23. Kuo J-Y, Lai T-Y, Fanjiang Y-Y, Huang F-C, Liao Y-H (2015) A behavior-based flame detection method for a real-time video surveillance system. J Chin Inst Eng 38:947–958CrossRefGoogle Scholar
  24. Leblon B (2005) Monitoring forest fire danger with remote sensing. Nat Hazards 35:343–359CrossRefGoogle Scholar
  25. Mahdipour E, Dadkhah C (2014) Automatic fire detection based on soft computing techniques: review from 2000 to 2010. Artif Intell Rev 42:895–934CrossRefGoogle Scholar
  26. National Emergency Management Agency (2012) The major statistics of emergency management. National Emergency Management Agency, SeoulGoogle Scholar
  27. Qureshi WS, Ekpanyapong M, Dailey MN, Rinsurongkawong S, Malenichev A, Krasotkina O (2016) QuickBlaze: early fire detection using a combined video processing approach. Fire Technol 52:1293–1317CrossRefGoogle Scholar
  28. Rong J, Zhou D, Yao W, Gao W, Chen J, Wang J (2013) Fire flame detection based on GICA and target tracking. Opt Laser Technol 47:283–291CrossRefGoogle Scholar
  29. San-Miguel-Ayanz J, Ravail N (2005) Active fire detection for fire emergency management: potential and limitations for the operational use of remote sensing. Nat Hazards 35:361–376CrossRefGoogle Scholar
  30. Saraf AK, Rawat V, Banerjee P, Choudhury S, Panda SK, Dasgupta S, Das JD (2008) Satellite detection of earthquake thermal infrared precursors in Iran. Nat Hazards 47:119–135CrossRefGoogle Scholar
  31. Sleights JE (2011) An evaluation of old armored cables in building wiring systems. Fire Technol 47:107–147CrossRefGoogle Scholar
  32. Truong TX, Kim J-M (2012) Fire flame detection in video sequences using multi-stage pattern recognition techniques. Eng Appl Artif Intell 25:1365–1372CrossRefGoogle Scholar
  33. Vidas S, Moghadam P (2013) HeatWave: a handheld 3D thermography system for energy auditing. Energy Build 66:445–460CrossRefGoogle Scholar
  34. Wang J, Wang H (2014) Tunable fiber laser based photoacoustic gas sensor for early fire detection. Infrared Phys Technol 65:1–4CrossRefGoogle Scholar
  35. Wang S-J, Jeng D-L, Tsai M-T (2009) Early fire detection method in video for vessels. J Syst Softw 82:656–667CrossRefGoogle Scholar
  36. Wang Y, Yu C, Tu R, Zhang Y (2011) Fire detection model in Tibet based on grey-fuzzy neural network algorithm. Expert Syst Appl 38:9580–9586CrossRefGoogle Scholar
  37. Wang S, He Y, Zou J, Duan B, Wang J (2014) A flame detection synthesis algorithm. Fire Technol 50:959–975CrossRefGoogle Scholar
  38. Ye S, Bai Z, Chen H, Bohush R, Ablameyko S (2017) An effective algorithm to detect both smoke and flame using color and wavelet analysis. Pattern Recogn Image Anal 27:131–138CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Architectural EngineeringYonsei UniversitySeoulSouth Korea
  2. 2.Division of Maintenance Bureau of Facility ManagementSeoul National UniversitySeoulSouth Korea

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