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Machine Vision Based Fire Detection Techniques: A Survey

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Abstract

The risk of fires is ever increasing along with the boom of urban buildings. The current methods of detecting fire with the use of smoke sensors with large areas, however poses an issue. The introduction of video surveillance systems has given a great opportunity for identifying smoke and flame from faraway locations and tackles this risk. Processing this huge amount of data is a problem with using these video and image data. In recent times, a number of methods have been proposed to deal with this challenge and identify fire and smoke. Image processing algorithms for detecting flame and smoke, motion-based estimation of smoke, etc are some of the methods that are proposed earlier. Recently, there has been an array of methods proposed using Deep Learning, Convolutional Neural Networks (CNNs) to automatically detect and predict flame and smoke in videos and images. In this paper, we present a complete survey and analysis of these machine vision based fire/smoke detection methods and their performance. Firstly, we introduce the fundamentals of image processing methods, CNNs and their application prospect in video smoke and fire detection. Next, the existing datasets and summary of the recent methodologies used in this field are discussed. Finally, the challenges and suggested improvements to further the development of the application of CNNs in this field are discussed. CNNs are shown to have a great potential for smoke and fire detection and better development can help prepare a robust system that would greatly save human lives and monetary wealth from getting destroyed from fires. Finally, research guidelines are presented to fellow researchers regarding data augmentation, fire and smoke detection models which need to be investigated in the future to make progress in this crucial area of research.

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References

  1. Brownlee J (2020) How do convolutional layers work in deep learning neural networks? Machine Learning Mastery. https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/, Accessed 12 Feb 2020

  2. Calderara S, Piccinini P, Cucchiara R (2010) Vision based smoke detection system using image energy and color information. Mach Vision Appl 22(4):705–719. https://doi.org/10.1007/s00138-010-0272-1

    Article  Google Scholar 

  3. Cetin AE (2008) Visifire dataset. Bilkent EE Signal Processing Group. http://signal.ee.bilkent.edu.tr/VisiFire/, Accessed 23 Feb 2020

  4. Chan WS, Burge JW (1996) Imaging flame detection system. US Patent 5937077A, 25 Apr 1996

  5. Chen T, Yin Y, Huang S, Ye Y (2006) The smoke detection for early fire-alarming system base on video processing. In: 2006 international conference on intelligent information hiding and multimedia, pp 427–430. https://doi.org/10.1109/IIH-MSP.2006.265033

  6. Chunyu Y, Jun F, Jinjun W, Yongming Z (2009) Video fire smoke detection using motion and color features. Fire Technol 46(3):651–663. https://doi.org/10.1007/s10694-009-0110-z

    Article  Google Scholar 

  7. DeepQuestAI (2019) Fireflame dataset. GitHub. https://github.com/DeepQuestAI/Fire-Smoke-Dataset. Accessed 22 Feb 2020

  8. Di Lascio R, Greco A, Saggese A, Vento M (2014) Improving fire detection reliability by a combination of videoanalytics. In: Image analysis and recognition, pp 477–484. https://doi.org/10.1007/978-3-319-11758-4_52

  9. Evarts B (2020) Fire loss in the united states during 2018. NFPA. https://www.nfpa.org/-/media/Files/News-and-Research/Fire-statistics-and-reports/US-Fire-Problem/osFireLoss.pdf. Accessed 10 Feb 2020

  10. Enis AC, Kosmas D, Benedict G, Nikos G, Osman G, Habiboglu YH, Toreyin BU, Steven V (2013) Video fire detection–review. Digital Signal Process 23(6):1827–1843 https://doi.org/10.1016/j.dsp.2013.07.003

    Article  Google Scholar 

  11. Filonenko A, Hernáindez DC, Jo K (2015) Smoke detection for static cameras. In: 2015 21st Korea–Japan joint workshop on frontiers of computer vision (FCV), pp 1–4. https://doi.org/10.1109/FCV.2015.7103719

  12. Filonenko A, Kurnianggoro L, Jo KH (2017) Smoke detection on video sequences using convolutional and recurrent neural networks. In: Computational collective intelligence. Springer, Berlin, pp 558–566. https://doi.org/10.1007/978-3-319-67077-5_54

  13. Foggia P, Saggese A, Vento M (2015) Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Trans Circuits Syst Video Technol 25:1545-1556 https://doi.org/10.1109/TCSVT.2015.2392531

    Article  Google Scholar 

  14. Gao Y, Cheng P (2019) Forest fire smoke detection based on visual smoke root and diffusion model. Fire Technol 55(5):1801–1826. https://doi.org/10.1007/s10694-019-00831-x

    Article  Google Scholar 

  15. Gaur A, Singh A, Kumar A, Kumar A, Kapoor K (2020) Video flame and smoke based fire detection algorithms: a literature review. Fire Technol 56(5):1943–1980. https://doi.org/10.1007/s10694-020-00986-y

    Article  Google Scholar 

  16. Gong F, Li C, Gong W, Li X, Yuan X, Ma Y, Song T (2019) A real-time fire detection method from video with multifeature fusion. Comput Intell Neurosci. https://doi.org/10.1155/2019/1939171

    Article  Google Scholar 

  17. Gu K, Xia Z, Qiao J, Lin W (2020) Deep dual-channel neural network for image-based smoke detection. IEEE Trans Multimed 22(2):311–323. https://doi.org/10.1109/tmm.2019.2929009

    Article  Google Scholar 

  18. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  19. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Computer vision—ECCV 2016. Springer International Publishing, Cham, pp 630–645. https://doi.org/10.1007/978-3-319-46493-0_38

  20. Horng WB, Peng JW (2008) A fast image-based fire flame detection method using color analysis. Tamkang J Sci Eng 11:273–285

    Google Scholar 

  21. Hu Y, Lu X (2018) Real-time video fire smoke detection by utilizing spatial-temporal convnet features. Multimed Tools Appl 77(22):29283–29301. https://doi.org/10.1007/s11042-018-5978-5

    Article  Google Scholar 

  22. Jadon A (2019) Firenet dataset. GitHub. https://github.com/arpit-jadon/FireNet-LightWeight-Network-for-Fire-Detection. Accessed 22 Feb 2020

  23. Jadon A, Omama M, Varshney A, Ansari MS, Sharma R (2019) Firenet: a specialized lightweight fire & smoke detection model for real-time iot applications. arXiv preprint arXiv:1905.11922

  24. Jia Y, Yuan J, Wang J, Fang J, Zhang Q, Zhang Y (2015) A saliency-based method for early smoke detection in video sequences. Fire Technol 52(5):1271–1292. https://doi.org/10.1007/s10694-014-0453-y

    Article  Google Scholar 

  25. Jian W, Wu K, Yu Z, Chen L (2018) Smoke regions extraction based on two steps segmentation and motion detection in early fire. In: MIPPR 2017: pattern recognition and computer vision, international society for optics and photonics, SPIE, vol 10609, pp 281–288. https://doi.org/10.1117/12.2285697

  26. Jiang X, Hu C, Fan Z, Zhang P (2015) Research on flame detection method by fusion feature and sparse representation classification. Int J Comput Commun Eng 5(4):238–245. https://doi.org/10.17706/ijcce.2016.5.4.238-245

    Article  Google Scholar 

  27. Jinlan L, Lin W, Ruliang Z, Chengquan H, Yan R (2016) A method of fire and smoke detection based on surendra background and gray bitmap plane algorithm. In: 2016 8th international conference on information technology in medicine and education (ITME), pp 370–374. https://doi.org/10.1109/ITME.2016.0089

  28. Kim B, Lee J (2019) A video-based fire detection using deep learning models. Appl Sci 9:2862. https://doi.org/10.3390/app9142862

    Article  Google Scholar 

  29. KMU-CVPR (2012) Kmu fire & smoke database. KMU CVPR Lab. https://cvpr.kmu.ac.kr/Dataset/Dataset.htm. Accessed 22 Feb 2020

  30. Ko B, Kwak JY, Nam JY (2012) Wildfire smoke detection using temporospatial features and random forest classifiers. Opt Eng 51(1):017208-1–017208-10. https://doi.org/10.1117/1.oe.51.1.017208

  31. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  32. Lee Y, Kim T, Shim J (2017) Smoke detection system research using fully connected method based on adaboost. J Multimed Inf Syst 4(2):79–82. https://doi.org/10.9717/JMIS.2017.4.2.79

    Article  Google Scholar 

  33. Liping Z, Hongqi L, Fenghui W, Jie L, Ali S, Hong Z (2018) A flame detection method based on novel gradient features. J Intell Syst 29(1):773–786. https://doi.org/10.1515/jisys-2017-0562

    Article  Google Scholar 

  34. Liu Z, Yang X, Liu Y, Qian Z (2019) Smoke-detection framework for high-definition video using fused spatial- and frequency-domain features. IEEE Access 7:89687–89701. https://doi.org/10.1109/access.2019.2926571

    Article  Google Scholar 

  35. Liu ZG, Yang Y, Ji XH (2015) Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the ycbcr color space. Signal Image Video Process 10(2):277–284. https://doi.org/10.1007/s11760-014-0738-0

    Article  Google Scholar 

  36. Luo S, Yan C, Wu K, Zheng J (2015) Smoke detection based on condensed image. Fire Safety J 75:23–35. https://doi.org/10.1016/j.firesaf.2015.04.002

    Article  Google Scholar 

  37. Luo Y, Zhao L, Liu P, Huang D (2018) Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimed Tools Appl 77:15075–15092. https://doi.org/10.1007/s11042-017-5090-2

    Article  Google Scholar 

  38. Marbach G, Loepfe M, Brupbacher T (2006) An image processing technique for fire detection in video images. Fire Saf J 41(4):285–289. https://doi.org/10.1016/j.firesaf.2006.02.001

    Article  Google Scholar 

  39. Matlani P, Shrivastava M (2017) A survey on video smoke detection. Inf Commun Technol Sustain Dev. https://doi.org/10.1007/978-981-10-3932-4_22

    Article  Google Scholar 

  40. Pundir AS, Raman B (2019) Dual deep learning model for image based smoke detection. Fire Technol 55(6):2419–2442. https://doi.org/10.1007/s10694-019-00872-2

    Article  Google Scholar 

  41. Qixing Z (2018) Sklfs dataset. Fire Detection Research Group. http://smoke.ustc.edu.cn/datasets.htm. Accessed 22 Feb 2020

  42. Razmi SM, Saad N, Asirvadam VS (2010) Vision-based flame analysis using motion and edge detection. In: 2010 international conference on intelligent and advanced systems, pp 1–4. https://doi.org/10.1109/ICIAS.2010.5716222

  43. Rouse M (2015) Matlab definition. WhatIs.com. https://whatis.techtarget.com/definition/MATLAB. Accessed 24 Feb 2020

  44. Russo AU, Deb K, Tista SC, Islam A (2018) Smoke detection method based on LBP and SVM from surveillance camera. In: 2018 international conference on computer, communication, chemical, material and electronic engineering (IC4ME2), pp 1–4. https://doi.org/10.1109/IC4ME2.2018.8465661

  45. Sharma J, Granmo OC, Goodwin M, Fidje JT (2017) Deep convolutional neural networks for fire detection in images. In: Engineering applications of neural networks. Springer, Cham, pp 183–193. https://doi.org/10.1007/978-3-319-65172-9_16

  46. Shi X, Lu N, Cui Z (2019) Smoke detection based on dark channel and convolutional neural networks. In: 2019 5th international conference on big data and information analytics (BigDIA), pp 23–28. https://doi.org/10.1109/BigDIA.2019.8802668

  47. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  48. Szegedy C, Wei Liu, Yangqing Jia, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  49. Tang T, Dai L, Yin Z (2017/09) Smoke image recognition based on local binary pattern. In: Proceedings of the 2017 5th international conference on mechatronics, materials, chemistry and computer engineering (ICMMCCE 2017). Atlantis Press, pp 1118–1123. https://doi.org/10.2991/icmmcce-17.2017.199

  50. Tao C, Zhang J, Wang P (2016) Smoke detection based on deep convolutional neural networks. In: 2016 international conference on industrial informatics—computing technology, intelligent technology, industrial information integration (ICIICII), pp 150–153. https://doi.org/10.1109/ICIICII.2016.0045

  51. Toulouse T, Rossi L, Campana A, Celik T, Akhloufi MA (2017) Computer vision for wildfire research: an evolving image dataset for processing and analysis. Fire Saf J 92:188–194. https://doi.org/10.1016/j.firesaf.2017.06.012

    Article  Google Scholar 

  52. Vijayalakshmi SR, Muruganand S (2018) Fire recognition based on sensor node and feature of video smoke. In: 2018 international conference on advanced computation and telecommunication (ICACAT), pp 1–7. https://doi.org/10.1109/ICACAT.2018.8933629

  53. Wang L, Li A (2017) Early fire recognition based on multi-feature fusion of video smoke. In: 2017 36th Chinese control conference (CCC), pp 5318–5323. https://doi.org/10.23919/ChiCC.2017.8028197

  54. Wang S, He Y, Yang H, Wang K, Wang J (2017) Video smoke detection using shape, color and dynamic features. J Intell Fuzzy Syst 33(1):305–313. https://doi.org/10.3233/jifs-161605

    Article  Google Scholar 

  55. Wang Y, Wu A, Zhang J, Zhao M, Li W, Dong N (2016) Fire smoke detection based on texture features and optical flow vector of contour. In: 2016 12th world congress on intelligent control and automation (WCICA), pp 2879–2883. https://doi.org/10.1109/WCICA.2016.7578611

  56. Wikipedia (2020a) Convolutional neural network. Wikipedia. https://en.wikipedia.org/wiki/Convolutional_neural_network. Accessed 10 Feb 2020

  57. Wikipedia (2020b) Cuda. Wikipedia. https://en.wikipedia.org/wiki/CUDA. Accessed 24 Feb 2020

  58. Woodford C (2020) How smoke detectors work. ExplainThatStuff. https://www.explainthatstuff.com/smokedetector.html. Accessed 10 Feb 2020

  59. Wu X, Lu X, Leung H (2018) A video based fire smoke detection using robust adaboost. Sensors 18(11):3780. https://doi.org/10.3390/s18113780

    Article  Google Scholar 

  60. Xu G, Zhang Q, Liu D, Lin G, Wang J, Zhang Y (2019) Adversarial adaptation from synthesis to reality in fast detector for smoke detection. IEEE Access 7:29471–29483. https://doi.org/10.1109/ACCESS.2019.2902606

    Article  Google Scholar 

  61. Xu G, Zhang Y, Zhang Q, Lin G, Wang Z, Jia Y, Wang J (2019a) Video smoke detection based on deep saliency network. Fire Saf J 105:277–285. https://doi.org/10.1016/j.firesaf.2019.03.004

    Article  Google Scholar 

  62. Xu Z, Wanguo W, Xinrui L, Bin L, Yuan T (2019b) Flame and smoke detection in substation based on wavelet analysis and convolution neural network. In: ICIAI 2019: proceedings of the 2019 3rd international conference on innovation in artificial intelligence, pp 248–252. https://doi.org/10.1145/3319921.3319962

  63. Xuan Truong T, Kim JM (2012) Fire flame detection in video sequences using multi-stage pattern recognition techniques. Eng Appl Artif Intell 25(7):1365–1372. https://doi.org/10.1016/j.engappai.2012.05.007

    Article  Google Scholar 

  64. Yadav G, Gupta V, Gaur V, Bhattacharya M (2012) Optimized flame detection using image processing based techniques. Indian J Comput Sci Eng 3:202-211

    Google Scholar 

  65. Ye W, Zhao J, Wang S, Wang Y, Zhang D, Yuan Z (2015) Dynamic texture based smoke detection using surfacelet transform and hmt model. Fire Safety J 73:91–101. https://doi.org/10.1016/j.firesaf.2015.03.001

    Article  Google Scholar 

  66. Yin M, Lang C, Li Z, Feng S, Wang T (2018) Recurrent convolutional network for video-based smoke detection. Multimed Tools Appl 78(1):237–256. https://doi.org/10.1007/s11042-017-5561-5

    Article  Google Scholar 

  67. Yin Z, Wan B, Yuan F, Xia X, Shi J (2017) A deep normalization and convolutional neural network for image smoke detection. IEEE Access 5:18429–18438. https://doi.org/10.1109/ACCESS.2017.2747399

    Article  Google Scholar 

  68. Yu C, Mei Z, Zhang X (2013) A real-time video fire flame and smoke detection algorithm. Procedia Eng 62:891–898. https://doi.org/10.1016/j.proeng.2013.08.140

    Article  Google Scholar 

  69. Yuan F (2011) Video-based smoke detection with histogram sequence of lbp and lbpv pyramids. Fire Safety J 46(3):132–139. https://doi.org/10.1016/j.firesaf.2011.01.001

    Article  Google Scholar 

  70. Yuan F (2020) Video smoke detection dataset. State Key Lab of Fire Science, University of Science and Technology of China. http://staff.ustc.edu.cn/~yfn/vsd.html

  71. Yuan F, Zhang L, Wan B, Xia X, Shi J (2018) Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition. Mach Vision Appl 30(2):345–358. https://doi.org/10.1007/s00138-018-0990-3

    Article  Google Scholar 

  72. Zeng J, Lin Z, Qi C, Zhao X, Wang F (2018) An improved object detection method based on deep convolution neural network for smoke detection. In: 2018 international conference on machine learning and cybernetics (ICMLC). vol 1, pp 184–189. https://doi.org/10.1109/ICMLC.2018.8527037

  73. Zhaa X, Ji H, Zhang D, Bao H (2018) Fire smoke detection based on contextual object detection. In: 2018 IEEE 3rd international conference on image, vision and computing (ICIVC), pp 473–476. https://doi.org/10.1109/ICIVC.2018.8492823

  74. Zhang F, Qin W, Liu Y, Xiao Z, Liu J, Wang Q, Liu K (2020) A dual-channel convolution neural network for image smoke detection. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-08551-8

    Article  Google Scholar 

  75. Zhong Z, Wang M, Shi Y, Gao W (2018) A convolutional neural network-based flame detection method in video sequence. Signal Image Video Process 12(8):1619–1627. https://doi.org/10.1007/s11760-018-1319-4

    Article  Google Scholar 

  76. Zhou Z, Shi Y, Gao Z, Li S (2016) Wildfire smoke detection based on local extremal region segmentation and surveillance. Fire Saf J 85:50–58. https://doi.org/10.1016/j.firesaf.2016.08.004

    Article  Google Scholar 

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Geetha, S., Abhishek, C.S. & Akshayanat, C.S. Machine Vision Based Fire Detection Techniques: A Survey. Fire Technol 57, 591–623 (2021). https://doi.org/10.1007/s10694-020-01064-z

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