Implementation of Computer Vision Based Industrial Fire Safety Automation by Using Neuro-Fuzzy Algorithms

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Manjunatha K.C. 1,* Mohana H.S 2 P.A Vijaya 2

1. M/s. Prakash Sponge Iron and Power (P) Ltd (ERM Group), Chitradurga - 577501, India

2. Dept. of IT & EC Malnad College of Engineering, Hassan - 573201, India

* Corresponding author.


Received: 2 Aug. 2014 / Revised: 3 Dec. 2014 / Accepted: 22 Jan. 2015 / Published: 8 Mar. 2015

Index Terms

Onsite Emergency System, SCADA, PLC, Weighted Centroid, Fire Pixel Number, Neuro-Fuzzy Algorithm


A computer vision-based automated fire detection and suppression system for manufacturing industries is presented in this paper. Automated fire suppression system plays a very significant role in Onsite Emergency System (OES) as it can prevent accidents and losses to the industry. A rule based generic collective model for fire pixel classification is proposed for a single camera with multiple fire suppression chemical control valves. Neuro-Fuzzy algorithm is used to identify the exact location of fire pixels in the image frame. Again the fuzzy logic is proposed to identify the valve to be controlled based on the area of the fire and intensity values of the fire pixels. The fuzzy output is given to supervisory control and data acquisition (SCADA) system to generate suitable analog values for the control valve operation based on fire characteristics. Results with both fire identification and suppression systems have been presented. The proposed method achieves up to 99% of accuracy in fire detection and automated suppression.

Cite This Paper

Manjunatha K.C., Mohana H.S, P.A Vijaya, "Implementation of Computer Vision Based Industrial Fire Safety Automation by Using Neuro-Fuzzy Algorithms", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.4, pp.14-27, 2015. DOI:10.5815/ijitcs.2015.04.02


[1]Tao Chen, Hongyong Yuan. An automatic fire searching and suppression system for large spaces. Elsevier Fire Safety Journal, 297-307, doi:10.1016/j.firesaf.2003. 11.007, 2004.

[2]MEI Zhibin, YU Chunyu. Machine Vision Based Fire Flame Detection Using Multi-Features. 24th Chinese Control and Decision Conference (CCDC), 2844-2848, 2012.

[3]A. Rehman, N. Masood. Autonomous Fire Extinguishing System. IEEE Transactions, 218-222, 2012.

[4]Hideaki Yamagishi. Fire Flame Detection Algorithm Using a Color Camera. IEEE international symposium on MHS,255-259, 1999.

[5]Paulo Vinicius Koerich Borges. A Probabilistic Approach for Vision-Based Fire Detection in Videos. IEEE transactions on circuits and systems for video technology, vol. 20, no. 5, 721-731, 2010.

[6]Bo-Ho Cho , Jong-Wook Bae. Image Processing-based Fire Detection System using Statistic Color Model. International Conference on Advanced Language Processing and Web Information Technology, 245-250, 2008.

[7]Mohammad Jane Alam Khan, Muhammed Rifat Imam. Automated Fire Fighting System with Smoke and Temperature Detection. IEEE International Conference on Electrical and Computer Engineering. 232-235, 2012.

[8]Suzilawati Mohd Razmi, Nordin Saad. Vision-Based Flame Detection: Motion Detection & Fire Analysis. IEEE Student Conference on Research and Development, 187-191, 2010.

[9]Andrey N. Pavlov, Evgeniy S. Povemov. Experimental Installation for Test of Automatic Fire Gas Explosion Suppression System. 10thB IEEE International Conference and Seminar EDM, 332-334, 2009.

[10]Turgay Celik, and Kai-Kuang Ma. Computer Vision Based Fire Detection in Color Images. IEEE Conference on Soft Computing in Industrial Applications, 258-263, 2008.

[11]Jie Hou, Jiaru Qian, Zuozhou Zhao. Fire Detection Algorithms in Video Images for High and Large-span Space Structures. IEEE Transactions, 2009.

[12]KuoL. Su. Automatic Fire Detection System Using Adaptive Fusion Algorithm for Fire Fighting Robot. IEEE International Conference on Systems Cybernetics, 966-971, 2006.

[13]Changwoo Ha, Ung Hwang. Vision-Based Fire Detection Algorithm Using Optical Flow. IEEE Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, 526-530, 2012.

[14]Tian Qiu, Yong Yan. An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing. IEEE Transactions on Instrumentation and Measurement, 1486-1493, 2012.

[15]Chen Jun, Du Yang, Wang Dong. An Early Fire Image Detection and Identification Algorithm Based on DFBIR Model. IEEE World Congress on Computer Science and Information Engineering, 229-232, 2009.

[16]And& Neubauer. Genetic Algorithms in Automatic Fire Detection Technology. Genetic Algorithms in Engineering Systems: Innovations and Applications, 180-185, 1997.

[17]T. Chen, P. Wu and Y. Chiou. An Early Fire-Detection Method Based on Image Processing. Proc. of IEEE ICIP ’04, 1707–1710, 2004.

[18]Lee and Dongil Han. Real-Time Fire Detection Using Camera Sequence Image in Tunnel Environment. Proceedings of ICIC, vol. 4681, 1209-1220, 2007.

[19]Ishita Chakraborty, Ishita Chakraborty. A Hybrid Clustering Algorithm for Fire Detection in Video and Analysis with Color based Thresholding Method. IEEE International Conference on Advances in Computer Engineering. 277-280, 2010.

[20]Dengyi Zhang, Shizhong Han. Image Based Forest Fire Detection Using Dynamic Characteristics With Artificial Neural Networks. IEEE International Joint Conference on Artificial Intelligence, 290-293, 2009.

[21]Quanmin GUO Junjie DAI. Study on Fire Detection Model Based on Fuzzy Neural Network. IEEE Transactions, 1-4, 2010.

[22]GUO Jian, ZHU Jie. Application of Self-Adaptive Neural Fuzzy Network in Early Detection of Conveyor Belt Fire. IEEE Transactions, 978-983, 2009.

[23]Turgay Çelik, Hüseyin Özkaramanl. Fire Pixel Classification Using Fuzzy Logic and Statistical Color Model. IEEE Transactions, 1205-1208, 2007.

[24]Ana Del Amo. Fuzzy Logic Applications to Fire Control Systems. IEEE International Conference on Fuzzy Systems, 1298-1304, 2006.

[25]Xuan Truong, Tung. Fire flame detection in video sequences using multi-stage pattern recognition techniques. Engineering Applications of Artificial Intelligence 1365-1372, 2010.

[26]Wirth,M. Zaremba,R. Flame region detection based on histogram backprojection. CRV 7th Canadian Conference on Computer and Robot Vision, 167-174, 2010.

[27]Xitao Zheng, Yongwei Zhang, Yehua Yu, Recognition of marrow cell images based in fuzzy clustering, International Journal of Information Technology and Computer Science (IJITCS), volume 1, Pages 40, 2012.

[28]Hadi A. Alnabriss, Ibrahim S. I. Abuhaiba, Improved Image Retrieval with Color and Angle Representation, International Journal of Information Technology and Computer Science (IJITCS), Pp 68-81, 2014.

[29]Amanpreet Singh, Preet Inder Singh, Prabhpreet Kaur, Digital Image Enhancement with Fuzzy Interface System, International Journal of Information Technology and Computer Science (IJITCS), Pages 51, 2012.