Skip to main content

Advertisement

SpringerLink
Log in

Towards Development of a Low Cost Early Fire Detection System Using Wireless Sensor Network and Machine Vision

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Fire is one of the most prominent threat to safety of human and property, in both domestic and industrial setups. Efficiently combating a fire threat, usually, depends on how early the fire is detected. This paper reports work for development of a low cost wireless sensor-based system for surveillance and early fire detection, using machine vision technique. The system consists of an on-board camera node, capable of transmitting videos over wireless network to a remote host computer that runs an image processing based fire detection algorithm. The system is standalone and portable with the capability of transmitting videos to virtually anywhere in the world. Prototype of the system has been successfully tested, performing video streaming alongwith segmentation of fire regions using HSI features of the retrieved images. Future work will inlcude automatic fire detection and alarm generation alongwith the extension of the system on multiple and widely scattered transmission nodes.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Çetin, E. A., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboǧlu, Y. H., et al. (2013). Video fire detection—review. Digital Signal Processing, 23, 1827–1843.

    Article  Google Scholar 

  2. Wang, S., & Chen, J. (2012). A method of video flame detection based on multi-feature fusion. Journal of Convergence Information Technology, 7(21), 634–642.

    Article  Google Scholar 

  3. Kim, Y.-H., Kim, A., & Jeong, H.-Y. (2014). Rgb color model based fire detection algorithm in video sequences on wireless sensor network. International Journal of Distributed Sensor Networks, 10, 1477–1550.

    Google Scholar 

  4. Shen, P., Zhang, L., Song, J., Xu, H., Zhou, L., Wei, W., et al. (2013). Dm642-based fire detection in video sequences using statistical color model. International Journal of Digital Content Technology and its Applications, 7(3), 669–678.

    Article  Google Scholar 

  5. Healey, G., Slater, D., Lin, T., Drda, B., & Goedeke, A. D. (1993). A system for real-time fire detection. In Proceedings of the IEEE conference on computer vision and pattern recognition.

  6. Hempstead, M., Lyons, M. J., Brooks, D., & Wei, G.-Y. (2008). Survey of hardware systems for wireless sensor networks. Journal of Low Power Electronics, 4(1), 1–10.

    Article  Google Scholar 

  7. Aslan, Y. E., Korpeoglu, I., & Ulusoy, Ö. (2012). A framework for use of wireless sensor networks in forest fire detection and monitoring. Elsevier Journal on Computers, Environment and Urban Systems, 36, 614–625.

    Article  Google Scholar 

  8. Alkhatib, A. A. A. (2013). Smart and low cost technique for forest fire detection using wireless sensor network. International Journal of Computer Applications, 81(11), 12–18.

    Article  Google Scholar 

  9. Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9, 8722–8747.

    Article  Google Scholar 

  10. Kurata, N., Spencer Jr., B. F., & Ruiz-Sandoval, M. (2004). Building risk monitoring using wireless sensor network. In Proceedings of the 13th world conferences on earthquake engineering.

  11. Al-Marakeby, A. (2013). Camera-based wireless sensor networks for e-health. International Journal of Advanced Research in Computer and Communication Engineering, 2, 4757–4761.

    Google Scholar 

  12. Stipaničev, D., Štula, M., Krstinić, D., Šerić, L., Jakovčević, T., & Bugarić, M. (2010). Advanced automatic wildfire surveillance and monitoring network. In Proceedings of the 6th international conference on forest fire research.

  13. True, N. (2016). Computer vision based fire detection. Online available at http://www.cseweb.ucsd.edu/classes/wi09/cse190-a/reports/ntrue.pdf. Accessed March, 2016.

  14. Kochláň, M., Hodoň, M., Čechovič, L., Kapitulík, J., & Jurečka, M. (2014). WSN for traffic monitoring using raspberry piboard. In: Proceedings of the federated conference on computer science and information systems.

  15. Lin, S. Y., Luo, R. C., & Su, K. L. (2003). A multi agent multi sensorbased security system for intelligent building. In Proceedings of the IEEE conference on multi sensor fusion and integration for intelligent systems.

  16. Muller, H. C., & Fischer, A. (1995). A robust fire detection algorithm for temperature and optical smoke density using fuzzy logic. In Proceedings of the 29th international conference on security technology (pp. 197–204).

  17. Bartoloni, A., Cisbani, E., Marchese, M., Efisei, G., & Salvati, A. (2002). Early fire detection system based on multi-temporal images of geostationary and polar satellites. In Proceedings of the IEEE international geoscience and remote sensing symposium.

  18. Odic, R. M., Jones, R. I., & Tatam, R. P. (2002). Distributed temperature sensor for aeronautic applications. In Optical fiber sensors conference technical digest.

  19. Arrue, B. C., Ollero, A., & Matinez de Dios, J. R. (2002). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems and Their Applications, 15(3), 64–73.

  20. Kaiser, T. (2000). Fire detection with temperature sensor arrays. In Proceedings of the 34th international conference on security technology.

  21. Luo, R. C., Su, K. L., & Tsai, K. H. (2002). Fire detection andisolation for intelligent building system using adaptive sensoryfusion method. In Proceedings of the international conference on robotics and automation.

  22. Neubauer, A. (1997). Genetic algorithms in automatic fire detection technology. In Proceedings of the 2nd international conference on genetic algorithms in engineering systems: Innovations and applications.

  23. Xiao, J.-M., & Wang, X.-H. (2003). A fuzzy neural network approach to fire detection in ships. In Proceedings of the 12th IEEE international conference on fuzzy systems.

  24. Sun, C. T., Jang, J. S. R., & Mizutani, E. (1997). Neuro fuzzy and soft computing. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  25. Noda, S., & Ueda, K. (1994). Fire detection in tunnels using animage processing method. In Proceedings of the IEEE conference on vehicle navigation and information systems.

  26. Foo, S. Y. (1995). A machine vision approach to detect and categorizehydrocarbon fires in aircraft dry bays and engine compartments. In Proceedings of the IEEE conference on industry applications.

  27. Foo, S. Y. (2000). A fuzzy logic approach to fire detection in aircraft dry bays and engine compartments. IEEE Transactions on Industrial Electronics, 47, 1161–1171.

    Article  Google Scholar 

  28. Yamagishi, H., & Yamaguchi, J. (1999). Fire flame detectionalgorithm using a color camera. In IEEE international symposium on micromechatronics and human science.

  29. Yamagishi, H., & Yamaguchi, J. (2000). A contour fluctuation data processing method for fire flame detection using a color camera. In Proceedings of the 26th IEEE annual conference on industrial electronics society.

  30. Phillips, W., Shah, M., & Da Vitoria Lobo, N. (2000). Flame recognition in video. In IEEE workshop on applications of computer vision.

Download references

Author information

Authors and Affiliations

  1. Hamdard Institute of Engineering and Technology, Hamdard University, Karachi, Pakistan

    Kehkashan Kanwal

  2. NED University of Engineering and Technology, Karachi, Pakistan

    Aasia Liaquat & Mansoor Mughal

  3. Design Engineering and Applied Research Lab, KINPOE (Affiliated with PIEAS), Karachi, Pakistan

    Abdul Rehman Abbasi

  4. Sir Syed University of Engineering and Technology, Karachi, Pakistan

    Muhammad Aamir

Authors
  1. Kehkashan Kanwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aasia Liaquat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mansoor Mughal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdul Rehman Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Aamir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kehkashan Kanwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kanwal, K., Liaquat, A., Mughal, M. et al. Towards Development of a Low Cost Early Fire Detection System Using Wireless Sensor Network and Machine Vision. Wireless Pers Commun 95, 475–489 (2017). https://doi.org/10.1007/s11277-016-3904-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3904-6

Keywords

Search

Navigation