Skip to main content
SpringerLink
Log in

Recent Advances in Fire Detection and Monitoring Systems: A Review

  • Conference paper
  • First Online:
Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1 (SETIT 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 146))

Abstract

Wildfires are one of the most impacting natural disasters, leading to a huge devastation of humans and the environment. Due to the rapid development of sensors and technologies as well as the success of computer vision algorithms new and complete solutions for automatic fire monitoring and detection have been exposed. However, in the past years, only few literature reviews have been proposed to cover researches until the year 2015. To fill this gap, we provide, in this paper, an up-to-date comprehensive review on this problem. First, we present a general description and a comparative analysis in terms of reliability, flexibility and efficiency, of these systems. Then, we expose vision-based methods for fire detection. Our main focus was on techniques based on deep convolutional neural networks (CNNs).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lee, W., Kim, S., Lee, Y.T., Lee, H.W., Choi, M.: Deep neural networks for wild fire detection with unmanned aerial vehicle. In: 2017 IEEE International Conference on Consumer Electronics (ICCE), pp. 252–253. IEEE (2017)

    Google Scholar 

  2. Dimitropoulos, K., Gunay, O., Kose, K., Erden, F., Chaabene, F., Tsalakanidou, F. … Cetin, E.: Flame detection for video-based early fire warning for the protection of cultural heritage. In: Euro-Mediterranean Conference, pp. 378–387. Springer, Berlin, Heidelberg (2012)

    Chapter  Google Scholar 

  3. San-Miguel-Ayanz, J., Ravail, N.: Active fire detection for fire emergency management: Potential and limitations for the operational use of remote sensing. Nat. Hazards 35(3), 361–376 (2005)

    Article  Google Scholar 

  4. Yuan, C., Zhang, Y., Zhixiang, L.: A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 45(7), 783–792 (2015)

    Article  Google Scholar 

  5. Alkhatib, A.A.A.: A review on forest fire detection techniques. Int. J. Distrib. Sens. Netw. 10(3), 597368 (2014)

    Article  Google Scholar 

  6. Mahdipour, E., Dadkhah, C.: Automatic fire detection based on soft computing techniques: review from 2000 to 2010. Artif. Intell. Rev. 42(4), 895–934 (2014)

    Article  Google Scholar 

  7. Çetin, A.E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboǧlu, Y.H., Verstockt, S.: Video fire detection–review. Digit. Signal Proc. 23(6), 1827–1843 (2013)

    Article  Google Scholar 

  8. Den Breejen, E., Breuers, M., Cremer, F., Kemp, R., Roos, M., Schutte, K., De Vries, J.S.: Autonomous forest fire detection. In: Proceedings of 3rd International Conference on Forest Fire Research, pp. 2003–2012 (1998)

    Google Scholar 

  9. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  10. Wilson, C.C., Davis, J.B.: Forest fire laboratory at riverside and fire research in California: past, present, and future. Gen. Tech. Rep. PSW-105, vol. 105, p. 22. Berkeley, Calif.: Pacific Southwest Research Station, Forest Service, US Department of Agriculture (1988)

    Google Scholar 

  11. Tranchitella, M., Fujikawa, S., Ng, T.L., Yoel, D., Tatum, D., Roy, P., Hinkley, E.: Using tactical unmanned aerial systems to monitor and map wildfires. In: AIAA Infotech@ Aerospace 2007 Conference and Exhibit, p. 2749 (2007)

    Google Scholar 

  12. Ambrosia, V.G., Wegener, S., Zajkowski, T., Sullivan, D.V., Buechel, S., Enomoto, F., Hinkley, E.: The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010). Geocarto Int. 26(2), 85–101 (2011)

    Article  Google Scholar 

  13. Merino, L., Caballero, F., de Dios, J.R.M., Maza, I., Ollero, A.: Automatic forest fire monitoring and measurement using unmanned aerial vehicles. In: Viegas, D.X. (ed.) Proceedings of the 6th International Congress on Forest Fire Research. Coimbra, Portugal (2010)

    Google Scholar 

  14. Zhang, Y., Jiang, J.: Bibliographical review on reconfigurable fault-tolerant control systems. Annu. Rev. Control. 32(2), 229–252 (2008)

    Article  Google Scholar 

  15. Martínez-de Dios, J.R., Merino, L., Caballero, F., Ollero, A.: Automatic forest-fire measuring using ground stations and unmanned aerial systems. Sensors 11(6), 6328–6353 (2011)

    Article  Google Scholar 

  16. Navarro, G., Caballero, I., Silva, G., Parra, P.C., Vázquez, Á., Caldeira, R.: Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. Int. J. Appl. Earth Obs. Geoinf. 58, 97–106 (2017)

    Article  Google Scholar 

  17. Ruiz, J.A.M., Riaño, D., Arbelo, M., French, N.H., Ustin, S.L., Whiting, M.L.: Burned area mapping time series in Canada (1984–1999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters. Remote Sens. Environ. 117, 407–414 (2012)

    Article  Google Scholar 

  18. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: A review. arXiv preprint arXiv:1807.0551 (2018)

  19. Zhao, Y., Ma, J., Li, X., Zhang, J.: Saliency detection and deep learning-based wildfire identification in uav imagery. Sensors 18(3), 712 (2018)

    Article  Google Scholar 

  20. Shen, D., Chen, X., Nguyen, M., Yan, W.Q.: Flame detection using deep learning. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR). IEEE (2018)

    Google Scholar 

  21. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  22. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)

  23. Zhang, Q., Xu, J., Xu, L., Guo, H.: Deep convolutional neural networks for forest fire detection. In: Proceedings of the 2016 International Forum on Management, Education and Information Technology Application. Atlantis Press (2016)

    Google Scholar 

  24. Di Lascio, R., Greco, A., Saggese, A., Vento, M.: Improving fire detection reliability by a combination of videoanalytics. In: International Conference Image Analysis and Recognition, pp. 477–484. Springer, Cham (2014)

    Google Scholar 

  25. Foggia, P., Saggese, A., Vento, M.: Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Trans. Circuits Syst. Video Technol. 25(9), 1545–1556 (2015)

    Article  Google Scholar 

  26. Kim, J.-H., Starr, J.W., Lattimer, B.Y.: Firefighting robot stereo infrared vision and radar sensor fusion for imaging through smoke. Fire Technol. 51(4), 823–845 (2015)

    Article  Google Scholar 

  27. Bosch, I., Serrano, A., Vergara, L.: Multisensor network system for wildfire detection using infrared image processing. Sci. World J., 1–10 (2013)

    Article  Google Scholar 

  28. Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S.W.: Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6, 18174–18183 (2018)

    Article  Google Scholar 

  29. Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., Baik, S.W.: Efficient deep CNN-based fire detection and localization in video surveillance applications. IEEE Trans. Syst., Man, Cybern.: Syst. 99, 1–16 (2018)

    Google Scholar 

  30. Chen, T.-H., Wu, P.-H., Chiou, Y.-C.: An early fire-detection method based on image processing. In: 2004 International Conference on Image Processing. ICIP ‘04, pp. 1707–1710. IEEE (2004)

    Google Scholar 

  31. Celik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009)

    Article  Google Scholar 

  32. Marbach, G., Loepfe, M., Brupbacher, T.: An image processing technique for fire detection in video images. Fire Saf. J. 41(4), 285–289 (2006)

    Article  Google Scholar 

  33. Ho, C.-C.: Machine vision-based real-time early flame and smoke detection. Meas. Sci. Technol. 20(4), 045502 (2009)

    Article  Google Scholar 

  34. Celik, T., Demirel, H., Ozkaramanli, H.: Automatic fire detection in video sequences. In: 2006 14th European Signal Processing Conference, pp. 1–5. IEEE (2006)

    Google Scholar 

  35. Yu, C., Mei, Z., Zhang, X.: A real-time video fire flame and smoke detection algorithm. Procedia Eng. 62, 891–898 (2013)

    Article  Google Scholar 

  36. Kim, Y.-J., Kim, E.-G.: Fire detection system using faster R-CNN. In: International Conference on Future Information & Communication Engineering, vol. 9, no. 1 (2017)

    Google Scholar 

  37. Zhang, Q.-X., et al.: Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Procedia Eng. 211, 441–446 (2018)

    Article  Google Scholar 

  38. Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.M., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In: IECON 2016 – 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE (2016)

    Google Scholar 

  39. Prema, C.E., Vinsley, S.S., Suresh, S.: Efficient flame detection based on static and dynamic texture analysis in forest fire detection. Fire Technol. 54(1), 255–288 (2018)

    Article  Google Scholar 

  40. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (NIPS 2012), pp. 1097–1105 (2012)

    Google Scholar 

  41. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)

    Google Scholar 

  42. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  43. Chu, T., Guo, X.: Remote sensing techniques in monitoring post-fire effects and patterns of forest recovery in boreal forest regions: a review. Remote Sens. 6(1), 470–520 (2013)

    Article  MathSciNet  Google Scholar 

  44. FireInfo. https://www.nifc.gov/fireInfo/nfn.htm. Accessed 9 July 2018

  45. Statistics. https://www.iii.org/fact-statistic/facts-statistics-wildfires. Accessed 4 July 2018

  46. AVHRR Homepage. http://noaasis.noaa.gov/NOAASIS/ml/avhrr.html. Accessed 4 July 2018

  47. MODIS Web. https://modis.gsfc.nasa.gov/. Accessed 4 July 2018

  48. Fire dataset. http://signal.ee.bilkent.edu.tr/VisiFire/. Accessed 9 July 2018

  49. UAVs. http://dronestpe.e-monsite.com/pages/un-drone-comment-ca-marche.html. Accessed 9 July 2018

  50. LandsatHome. https://landsat.gsfc.nasa.gov/the-thematic-mapper/. Accessed 10 Sept 2018

  51. Flickr dataset. http://conteudo.icmc.usp.br/pessoas/junio/DatasetFlicker/DatasetFlickr.htm/. Accessed 9 July 2018

  52. Firesense dataset. https://zenodo.org/record/836749#.W22IN870mUk. Accessed 9 July 2018

  53. SPOT Homepage. http://www.spot-vegetation.com/index.html. Accessed 10 Sept 2018

  54. Sentinel Homepage. https://sentinel.esa.int/web/sentinel/home. Accessed 10 Sept 2018

Download references

Acknowledgements

This project is carried out under the MOBIDOC scheme, funded by the EU through the EMORI program and managed by the ANPR.

Author information

Authors and Affiliations

  1. SERCOM, Ecole Polytechnique de Tunisie, Université de Carthage, B.P.743, 2078, La Marsa, Tunisia

    Rafik Ghali, Marwa Jmal, Wided Souidene Mseddi & Rabah Attia

  2. Telnet Innovation Labs, Telnet Holding, Ariana, Tunisia

    Rafik Ghali & Marwa Jmal

Authors
  1. Rafik Ghali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marwa Jmal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wided Souidene Mseddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rabah Attia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rafik Ghali , Marwa Jmal , Wided Souidene Mseddi or Rabah Attia .

Editor information

Editors and Affiliations

  1. SETIT Lab, University of Sfax, Sfax, Tunisia

    Med Salim Bouhlel

  2. DIBRIS - University of Genoa, Genova, Genova, Italy

    Stefano Rovetta

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghali, R., Jmal, M., Souidene Mseddi, W., Attia, R. (2020). Recent Advances in Fire Detection and Monitoring Systems: A Review. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_32

Download citation

Publish with us

Policies and ethics

Search

Navigation