FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-100342

Research Article |

Autoadaptive Flame Detection and Classification Using Deep Learning of FastFlameNet CNN

Author(s): S Sruthi1 and Dr. B Anuradha2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 3

Publisher : FOREX Publication

Published : 22 September 2022

e-ISSN : 2347-470X

Page(s) : 670-676

DOI: https://doi.org/10.37391/IJEER.100342

Abstract

Image processing technologies in the domain of pattern recognition have many successful researches and implementations. In that sequence, earlier detection of fire from the video footage of the surveillance cameras is an interesting and promising technique that serves mankind and nature as well. The traditional and existing methods of fire detection in the video frames are advantageous in industry-based applications. But whereas these techniques are applied to detect forest fire in a wider area, they have their limitations of inadequate output due to interferences caused by the sunlight and other natural attributes. To improve the detection efficiency using optical flow algorithms and to estimate the direction of the flame, a novel flame detection technique from the video frames using Optimal flow algorithm and the estimation of the fire flow direction using the Deep learning CNN FastFlameNet algorithm is explained in detail in this article. The performance of the proposed architecture is measured using the performance indices like Accuracy, precision, recall, F-Measure. It was estimated that about 97% of the performance accuracy was obtained from the proposed framework.

Keywords: Flame detection, Fire segmentation, CNN, Classification, Optical flow




S Sruthi, Department of ECE, SVU College of Engineering, SV University, Tirupati, India; Email: ssruthidtb@gmail.com

Dr. B Anuradha, Professor, Department of ECE, SVU College of Engineering, SV University, India; Email: anubhuma@yahoo.com

    [1] Cal Fire, 2018, [online] Available: https://www.fire.ca.gov/incidents/2018/ (accessed on Aug 10, 2020)[Cross Ref]
    [2] Z. Zhang, Y. Hu, “Video Smoke Detection Based on Convolution Neural Network”. In 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC)[Cross Ref]
    [3] S.S. Nalawade, “Fire Detection System using RGB Color Model.” In IJESC (2018), Volume 8 Issue No.5.[Cross Ref]
    [4] K. Muhammad, J. Ahmad, z. Lv and P. Bellavista, “Efficient Deep CNN- Based Fire Detection and Localization in Video Surveillance Applications.” In IEEE Transactions on Systems, Man, and Cybernetics: Systems ¦ March 2018[Cross Ref]
    [5] Q. Zhang, J. Xu, L. XU and H.Guo, “Deep Convolutional Neural Networks for Forest Fire Detection.” In IFMEITA 2016.[Cross Ref]
    [6] O. Maksymiv, T. Rak and D. Deleshko, “Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence.” In 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM)[Cross Ref]
    [7] K. Wang, J. Wang, Y. Zhang, Q. Zhang, B. Chen and D. Liu, “Fire Detection in Infrared Video Surveillance Based on Convolutional Neural Network and SVM.” In 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP)[Cross Ref]
    [8] J. Sharma, O. C. Granmo, M. Goodwin and J. T. Fidje, “Deep convolutional neural networks for fire detection in images”. In International Conference on Engineering Applications of Neural Networks (2017)[Cross Ref]
    [9] W.Liu, A. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu and A. Berg, “SSD: Single Shot MultiBox Detector.” In ECCV (2016).[Cross Ref]
    [10] Huy X. Pham, Hung M. La, David Feil-Seifer, Matthew Deans, “A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking” In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)[Cross Ref]
    [11] C. Yuan, Z. Liu and Y. Zhang. “Fire detection using infrared images for UAV-based forest fire surveillance”. In 2017 International Conference on Unmanned Aircraft Systems (ICUAS)[Cross Ref]
    [12] Yuan, Chi & Zhang, Youmin & Liu, Zhixiang. (2015) "A Survey on Technologies for Automatic Forest Fire Monitoring, Detection and Fighting Using UAVs and Remote Sensing Techniques". Canadian Journal of Forest Research. 45. 150312143318009. 10.1139/cjfr-2014-0347. [Cross Ref]
    [13] Chen TH, Wu PH, Chiou YC (2004) An early fire-detection method based on image processing. In: 2004 International conference on image processing, 2004 (ICIP’04), vol.3, pp. 1707–1710. IEEE[Cross Ref]
    [14] Marbach G, Loepfe M, Brupbacher T (2006) An image processing technique for fire detection in video images. Fire Safety J 41(4):285–289[Cross Ref]
    [15] Celik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Safety J 44(2):147–158[Cross Ref]
    [16] Celik T (2010) Fast and efficient method for fire detection using image processing.ETRI J 32(6):881–890[Cross Ref]
    [17] Celik T, Demirel H, Ozkaramanli H, Uyguroglu M (2007) Fire detection using statistical color model in video sequences. J Vis Commun Image Represent 18(2):176–185[Cross Ref]
    [18] Rinsurongkawong S, Ekpanyapong M, Dailey MN (2012) Fire detection for early fire alarm based on optical flow video processing. In: 2012 9th International conference on electrical engineering/electronics, computer, telecommunications and information technology,pp. 1-4, IEEE[Cross Ref]
    [19] W. Phillips, III, M. Shah, and N. da Vitoria Lobo, ―Flame recognition in video,Pattern Recognit. Lett., vol. 23, nos. 1–3, pp. 319–327, 2002.[Cross Ref]
    [20] Y. Habibo˘glu, O. Günay, and A. Çetin, Covariance matrix-based fire and flame detection method in video,Mach. Vis. Appl., vol. 23, no. 6,pp. 1–11, 2011.[Cross Ref]
    [21] S. Fazekas and D. Chetverikov, ―Analysis and performance evaluation of optical flow features for dynamic texture recognition, Signal Process., Image Commun., vol. 22, nos. 7–8, pp. 680–691, 2007.[Cross Ref]
    [22] D. Chetverikov and R. Péteri, ―A brief survey of dynamic texture description and recognition,in Proc. Int. Conf. Comput. Recognit. Syst.,[Cross Ref]
    [23] S. Fazekas, T. Amiaz, D. Chetverikov, and N. Kiryati, ―Dynamic texture detection based on motion analysis,Int. J. Comput. Vis., vol. 82, no. 1, pp. 48–63, 2009.[Cross Ref]
    [24] D. Chetverikov, S. Fazekas, and M. Haindl, Dynamic texture as foreground and background, Mach. Vis. Appl., vol. 22, no. 5, pp. 741–750,2011.[Cross Ref]
    [25] Y. Chunyu, F. Jun, W. Jinjun, and Z. Yongming, ―Video fire smoke detection using motion and color features, Fire Technol., vol. 46, no. 3,pp.651–663, 2010.[Cross Ref]
    [26] B. Lucas and T. Kanade, ―An iterative image registration technique with an application to stereo vision, in Proc. Int. Joint Conf. Artif.Intell.,vol. 2. 1981, pp. 674–679.[Cross Ref]
    [27] P. Saisan, G. Doretto, Y. Wu, and S. Soatto, ―Dynamic texture recognition,in Proc. Conf. Comput. Vis. Pattern Recognit., vol. 2. 2001, pp. 58–63.[Cross Ref]
    [28] G. Doretto, A. Chiuso, Y. Wu, and S. Soatto, ―Dynamic textures, Int. J. Comput. Vis., vol. 51, no. 2, pp. 91–109, 2003.[Cross Ref]
    [29] L. Cooper, J. Liu, and K. Huang, ―Spatial segmentation of temporal texture using mixture linear models,in Proc. Int. Conf. Dyn. Vis., 2007, pp.142–150.[Cross Ref]
    [30] A. Chan and N. Vasconcelos, ―Modeling, clustering, and segmenting video with mixtures of dynamic textures,IEEE Trans. Pattern Anal.Mach. Intell., vol. 30, no. 5, pp. 909–926, May 2008.[Cross Ref]
    [31] R. Vidal and A. Ravichandran, ―Optical flow estimation & segmentation of multiple moving dynamic textures,in Proc. IEEE Comput. Soc.Conf. Comput. Vis.Pattern Recognit. vol. 2. Jun. 2005, pp. 516–521.[Cross Ref]
    [32] Seong-Hyun Kim and Eui-Rim Jeong (2022), 1-Dimensional Convolutional Neural Network Based Blood Pressure Estimation with Photo plethysmography Signals and Semi-Classical Signal Analysis. IJEER 10(2), 214-217. DOI: 10.37391/IJEER.100228.[Cross Ref]
    [33] Harendra Singh, Roop Singh, Parul Goel, Anil Singh and Naveen Sharma (2022), Automatic Framework for Vegetable Classification using Transfer-Learning. IJEER 10(2), 405-410. DOI: 10.37391/IJEER.100257.[Cross Ref]

S Sruthi and Dr. B Anuradha (2022), Autoadaptive Flame Detection and Classification Using Deep Learning of FastFlameNet CNN. IJEER 10(3), 670-676. DOI: 10.37391/IJEER.100342.