Skip to main content

Advertisement

SpringerLink
Log in

A system to detect potential fires using a thermographic camera

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • 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 50:1021–1040

    Google Scholar 

  • Çelik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44:147–158

    Article  Google Scholar 

  • Çetin AE, Dimitropoulos K, Gouverneur B, Grammalidis N, Günay O, Habiboǧlu YH, Töreyin BU, Verstockt S (2013) Video fire detection—review. Digit Signal Proc 23:1827–1843

    Article  Google Scholar 

  • Cheng C, Sun F, Zhou X (2011) One fire detection method using neural networks. Tsinghua Sci Technol 16:31–35

    Article  Google Scholar 

  • 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–S161-167

    Article  Google Scholar 

  • Emmy Prema C, Vinsley SS, Suresh S (2016) Multi feature analysis of smoke in YUV color space for early forest fire detection. Fire Technol 52:1319–1342

    Article  Google Scholar 

  • 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 166:97–114

    Article  Google Scholar 

  • Gade R, Moeslund TB (2014) Thermal cameras and applications: a survey. Mach Vis Appl 25:245–262

    Article  Google Scholar 

  • Grapinet M, De Souza P, Smal JC, Blosseville JM (2013) Characterization and simulation of optical sensors. Accid Anal Prev 60:344–352

    Article  Google Scholar 

  • Gutmacher D, Hoefer U, Wöllenstein J (2012) Gas sensor technologies for fire detection. Sens Actuat B Chem 175:40–45

    Article  Google Scholar 

  • 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 231:497–505

    Article  Google Scholar 

  • 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 1:1419

    Article  Google Scholar 

  • Isaiah P, Shima T (2015) Motion planning algorithms for the dubins travelling salesperson problem. Automatica 53:247–255

    Article  Google Scholar 

  • 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 13:328–338

    Article  Google Scholar 

  • 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 41:1031–1041

    Article  Google Scholar 

  • 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 52:1271–1292

    Article  Google Scholar 

  • 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 95:475

    Article  Google Scholar 

  • Karakus C, Gurbuz AC, Tavli B (2013) Analysis of energy efficiency of compressive sensing in wireless sensor networks. Sens J IEEE 13:1999–2008

    Article  Google Scholar 

  • Khodayar F, Sojasi S, Maldague X (2016) Infrared thermography and NDT: 2050 horizon. Quant InfraRed Thermogr J 13:210–231

    Article  Google Scholar 

  • 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 45:262–270

    Article  Google Scholar 

  • 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 79:37–43

    Article  Google Scholar 

  • 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 91:1059–1067

    Article  Google Scholar 

  • 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 38:947–958

    Article  Google Scholar 

  • Leblon B (2005) Monitoring forest fire danger with remote sensing. Nat Hazards 35:343–359

    Article  Google Scholar 

  • Mahdipour E, Dadkhah C (2014) Automatic fire detection based on soft computing techniques: review from 2000 to 2010. Artif Intell Rev 42:895–934

    Article  Google Scholar 

  • National Emergency Management Agency (2012) The major statistics of emergency management. National Emergency Management Agency, Seoul

    Google Scholar 

  • 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 52:1293–1317

    Article  Google Scholar 

  • 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 47:283–291

    Article  Google Scholar 

  • 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 35:361–376

    Article  Google Scholar 

  • 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 47:119–135

    Article  Google Scholar 

  • Sleights JE (2011) An evaluation of old armored cables in building wiring systems. Fire Technol 47:107–147

    Article  Google Scholar 

  • Truong TX, Kim J-M (2012) Fire flame detection in video sequences using multi-stage pattern recognition techniques. Eng Appl Artif Intell 25:1365–1372

    Article  Google Scholar 

  • Vidas S, Moghadam P (2013) HeatWave: a handheld 3D thermography system for energy auditing. Energy Build 66:445–460

    Article  Google Scholar 

  • Wang J, Wang H (2014) Tunable fiber laser based photoacoustic gas sensor for early fire detection. Infrared Phys Technol 65:1–4

    Article  Google Scholar 

  • Wang S-J, Jeng D-L, Tsai M-T (2009) Early fire detection method in video for vessels. J Syst Softw 82:656–667

    Article  Google Scholar 

  • Wang Y, Yu C, Tu R, Zhang Y (2011) Fire detection model in Tibet based on grey-fuzzy neural network algorithm. Expert Syst Appl 38:9580–9586

    Article  Google Scholar 

  • Wang S, He Y, Zou J, Duan B, Wang J (2014) A flame detection synthesis algorithm. Fire Technol 50:959–975

    Article  Google Scholar 

  • 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 27:131–138

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Architectural Engineering, Yonsei University, Seoul, 03722, South Korea

    Chijoo Lee

  2. Division of Maintenance Bureau of Facility Management, Seoul National University, Seoul, 08826, South Korea

    Hyungjun Yang

Authors
  1. Chijoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyungjun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyungjun Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, C., Yang, H. A system to detect potential fires using a thermographic camera. Nat Hazards 92, 511–523 (2018). https://doi.org/10.1007/s11069-018-3224-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-018-3224-0

Keywords

Search

Navigation