Fire Detection Based on Image Using MATLAB GUI Programme

Muhammad Dhafier Mu'afa, Mark Reindhard Joyakin Silalahi, Hanif Wisti Julitama, Joko Hariyono

Abstract

Computer vision-based fire detection systems overcome this limitation in that they do not identify flammability on a product-by-product basis. In this study, fire detection was carried out using the YCbCr, RGB, and HSV map approach. The offered system uses color segmentation as a component of fire detection analysis. These three colors space segments will then be extracted to determine the presence of fire in the image used. A rule which consists of five rules based on color space condition had been constructed for classification of a pixel classified as fire. If a pixel satisfies these five rules, the pixels belong to fire class.This paper consists of 6 steps, including image acquisition, image pre-processing, image segmentation, feature extraction, image classification, and GUI creation. GUI provides a visual interface that is intuitive and easy for the user to understand the proposed system. By using button and another visual elements, users can interact with the system efficiently. Based on the tests carried out, the proposed system can detect images of fire in dark and light conditions. Performance testing is done by collecting a set of fire images on the internet. Performance is judged based on how many errors are generated when detecting fire. Performance is categorized into five types, including very good, good, fair, poor, and very poor.

Full Text:

PDF

References

M. Li, W. Xu, K. Xu, J. Fan, and D. Hou, “Review of fire detection technologies based on video image,” J. Theor. Appl. Inf. Technol., vol. 49, no. 2, pp. 700–707, 2013, [Online]. Available: http://dx.doi.org/

T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu, “Fire detection using statistical color model in video sequences,” J. Vis. Commun. Image Represent., vol. 18, no. 2, pp. 176–185, 2007, doi: 10.1016/j.jvcir.2006.12.003.

Y.-J. Kim and E.-G. Kim, “Image based Fire Detection using Convolutional Neural Network,” J. Korea Inst. Inf. Commun. Eng., vol. 20, no. 9, pp. 1649–1656, 2016, doi: 10.6109/jkiice.2016.20.9.1649.

G. Yadav, V. Gupta, and V. Gaur, “Optimized Flame Detection Using Image Processing Based,” Indian J. Comput. Sci. Eng., vol. 3, no. 2, pp. 202–211, 2012.

E. Hassan and A. Aboshgifa, “Detecting Brain Tumour from Mri Image Using Matlab GUI Programme,” Int. J. Comput. Sci. Eng. Surv., vol. 6, no. 6, pp. 47–60, 2015, doi: 10.5121/ijcses.2015.6604.

M. S. Nasution and N. Fadillah, “Deteksi Kematangan Buah Tomat Berdasarkan Warna Buah dengan Menggunakan Metode YCbCr,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 3, no. 2, pp. 147–150, 2019, doi: 10.30743/infotekjar.v3i2.1059.

G. Nagalakshmi and S. Jyothi, “Image Acquisition , Noise removal , Edge Detection Methods in Image Processing Using Matlab for Prawn Species Identification,” no. May, pp. 325–331, 2015.

G. Li, G. Lu, and Y. Yan, “Fire detection using stereoscopic imaging and image processing techniques,” in 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings, 2014, pp. 28–32. doi: 10.1109/IST.2014.6958440.

V. Venugopal, “Image Processing Based Forest Fire Detection,” Int. J. Adv. Res. Eng. Technol., vol. 2, 2012.

N. Ibraheem, M. Hasan, R. Z. Khan, and P. Mishra, “Understanding Color Models: A Review,” ARPN J. Sci. Technol., vol. 2, 2012.

O. Marques, “Morphological Image Processing,” Pract. Image Video Process. Using MATLAB®, vol. 8491, pp. 299–334, 2011, doi: 10.1002/9781118093467.ch13.

N. Ya’acob, M. S. M. Najib, N. Tajudin, A. L. Yusof, and M. Kassim, “Image Processing Based Forest Fire Detection using Infrared Camera,” J. Phys. Conf. Ser., vol. 1768, no. 1, p. 12014, Jan. 2021, doi: 10.1088/1742-6596/1768/1/012014.

M. A. I. Mahmoud and H. Ren, “Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation,” Math. Probl. Eng., vol. 2018, p. 7612487, 2018, doi: 10.1155/2018/7612487.

P. Chmelar and A. Benkrid, “Efficiency of HSV over RGB Gaussian Mixture Model for fire detection,” in 2014 24th International Conference Radioelektronika, 2014, pp. 1–4. doi: 10.1109/Radioelek.2014.6828426.

J. Seebamrungsat, S. Praising, and P. Riyamongkol, “Fire detection in the buildings using image processing,” in 2014 Third ICT International Student Project Conference (ICT-ISPC), 2014, pp. 95–98. doi: 10.1109/ICT-ISPC.2014.6923226.

M. Waghmare and P. M. Annamalai, “Fire Detection Based on Color , Shape and Motion,” Int. Res. J. Eng. Technol., vol. 4, no. 7, pp. 1804–1806, 2017, [Online]. Available: https://irjet.net/archives/V4/i7/IRJET-V4I7383.pdf

DOI: https://doi.org/10.20961/jeeict.5.1.69091

Refbacks

  • There are currently no refbacks.