Details

Developing an Integrated Computer Vision Image Processing Technique to raise an Early Alarm for Fire Detection

Ariz Abbas Naqvi

Department of Liberal Arts, Aligarh Muslim University, Aligarh, India

14-19

Vol: 11, Issue: 4, 2021

Receiving Date: 2021-07-18 Acceptance Date:

2021-09-24

Publication Date:

2021-10-10

Download PDF

http://doi.org/10.37648/ijrst.v11i04.002

Abstract

In Computer Vision and image classification task, convolutional neural networks (CNNs) have demonstrated high performance. Their use in fire detection systems will make detection much more accurate, reducing the number of fire disasters and their ecological and social effects. However, implementation in real-world surveillance networks of CNN-based fire detection systems poses the greatest risk due to their high inference memory and computational requirements. An original, energy-efficient, and computationally efficient design is presented in this paper.

Keywords: convolutional neural networks, fire detection; Computer Vision Image Processing Technique

References

  1. A. AAAlkhatib, (2013), Smart and Low Cost Technique for Forest Fire Detection using Wireless Sensor Network, Int. J. Comput. Appl., vol. 81, no. 11, pp. 12–18.
  2. J. Zhang, W. Li, Z. Yin, S. Liu, and X. Guo, (2009), Forest fire detection system based on wireless sensor network, 4th IEEE Conf. Ind. Electron. Appl. ICIEA 2009, pp. 520– 523.
  3. A. A. A. Alkhatib, (2014), A review on forest fire detection techniques, Int. J. Distrib. Sens. Netw., vol. 2014, no. March, 2014.
  4. P. Skorput, S. Mandzuka, and H. Vojvodic, (2016), The use of Unmanned Aerial Vehicles for forest fire monitoring, in 2016 International Symposium ELMAR, pp. 93–96.
  5. F. Afghah, A. Razi, J. Chakareski, and J. Ashdown, (2019), Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles, IEEE INFOCOM WKSHPS: MiSARN 2019: Mission-Oriented Wireless Sensor, UAV and Robot Networking
  6. Hanh Dang-Ngoc and Hieu Nguyen-Trung, (2019), Evaluation of Forest Fire Detection Model using Video captured by UAVs, presented at the 2019 19th International Symposium on Communications and Information Technologies (ISCIT), pp. 513–518.
  7. C. Kao and S. Chang, (2003), An Intelligent Real-Time Fire-Detection Method Based on Video Processing, IEEE 37th Annu. 2003 Int. Carnahan Conf. On Security Technol. 2003 Proc., 2003.
  8. C. E. Premal and S. S. Vinsley, (2014), Image Processing Based Forest Fire Detection using YCbCr Colour Model, Int. Conf. Circuit Power Comput. Technol. ICCPCT, vol. 2, pp. 87–95.
  9. C. Ha, U. Hwang, G. Jeon, J. Cho, and J. Jeong, (2012), Vision-based fire detection algorithm using optical flow, Proc. - 2012 6th Int. Conf. Complex Intell. Software Intensive Syst. CISIS 2012, pp. 526–530.
  10. K. Poobalan and S. Liew, (2105), Fire Detection Algorithm Using Image Processing Techniques, Proceeding 3rd Int. Conf. Artif. Intell. Comput. Sci., no. December, pp. 12– 13.
Back

Disclaimer: All papers published in IJRST will be indexed on Google Search Engine as per their policy.

We are one of the best in the field of watches and we take care of the needs of our customers and produce replica watches of very good quality as per their demands.