Fire Detection Using Image Processing

Authors

  • B. Swarajya Lakshmi Assistant Professor, Department of Computer Science and Engineering, Santhiram Engineering College, Nandyal, Andhra Pradesh, India

DOI:

https://doi.org/10.51983/ajcst-2021.10.2.2883

Keywords:

Amharic, Fake News, Machine Learning, Natural Language Processing

Abstract

Fire disasters have always been a threat to homes and businesses even with the various systems in place to prevent them. They cause property damage, injuries and even death. Preparedness is vital when dealing with fires. They spread uncontrollably and are difficult to contain. To contain them it is necessary for the fire to be detected early. Image fire detection heavily relies on an algorithmic analysis of images. However, the accuracy is lower, the detection is delayed and in common detection algorithms a large number of computation, including the image features being extracted manually and using machine. Therefore, in this paper, novel image detection which will be based on the advanced object detection like CNN model of YOLO v3 is proposed. The average precision of the algorithm based on YOLO v3 reaches to 81.76% and also it has the stronger robustness of detection performance, thereby satisfying the requirements of the real-time detection.

References

Ko, Byoung Chul and Sooyeong, Kwak, “Survey of computer vision-based natural disaster warning systems,” Opt. Eng. Vol. 51, No. 7, pp. 070901, 28 June 2012.

Thou-Ho Chen, Ping-Hsueh Wu and Yung-Chuen Chiou, “An early fire-detection method based on image processing,” 2004 International Conference on Image Processing, 2004. ICIP’04, Singapore, Vol. 3, pp. 1707-1710, 2004. DOI: 10.1109/ICIP.2004.1421401.

Ko, Byoungchul, Cheong, Kwang-Ho and Nam, Jae-Yeal, Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks. Fire Safety Journal, Vol. 45, 2010. DOI: 10.1016/j.firesaf.2010.04.001.

X. Qi, and Ebert, Jessica. A computer vision-based method for fire detection in color videos. International Journal of Imaging, Vol. 2, pp. 22-34. 2009.

J. Zhang, J. Zhuang, H. Du, S. Wang and X. Li. A Flame Detection Algorithm Based on Video Multi- feature Fusion. In: Jiao L., Wang L., Gao X., Liu J., Wu F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Vol. 4222. 2006.

Z. Yin, B. Wan, F. Yuan, X. Xia and J. Shi, “A Deep Normalization and Convolutional Neural Network for Image Smoke Detection,” in IEEE Access, Vol. 5, pp. 18429-18438, 2017. DOI: 10.1109/ACCESS.2017.2747399.

K. Muhammad, J. Ahmad, I. Mehmood, S. Rho and S. W. Baik, “Convolutional Neural Networks Based Fire Detection in Surveillance Videos,” in IEEE Access, Vol. 6, pp. 18174-18183, 2018, DOI: 10.1109/ACCESS.2018.2812835.

Li, Pu and Zhao, Wangda. Image fire detection algorithms based on convolutional neural networks. Case Studies in Thermal Engineering. Vol. 19, pp. 100625, 2020. DOI: 10.1016/j.csite.2020.100625.

Downloads

Published

05-11-2021

How to Cite

Swarajya Lakshmi, B. (2021). Fire Detection Using Image Processing. Asian Journal of Computer Science and Technology, 10(2), 14–19. https://doi.org/10.51983/ajcst-2021.10.2.2883