Abstract
This review is focused on video flame and smoke based fire detection algorithms for both indoor and outdoor environments. It analyzes and discusses them in a taxonomical manner for the last two decades. These are mainly based on handcraft features with or without classifiers and deep learning approaches. The separate treatment is provided for detecting flames and smoke. Their static and dynamic characteristics are elaborated for the handcraft feature approach. The blending of the obtained features from these characteristics is the focus of most of the research and these concepts are analyzed critically. A fusion of both visible and thermal images leading to multi-fusion and multimodal approaches have conversed. It is a step towards obtaining accurate detection results and how the handcraft feature approach tackles the problems of flame and smoke detection, as well as their weaknesses are discussed which are still not solved. Some of these weaknesses can be tackled by developing a technology based on artificial intelligence named deep-learning. Its taxonomical literature study with a focus on the flame and smoke detection is presented. The strengths and weaknesses of this approach are discussed with possible solutions. The latest trend in literature which focuses on the hybrid approach utilizing both handcraft feature, and deep learning approaches is discussed. This approach aims to minimize the weaknesses still present in the current systems.
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References
Brushlinsky NN, Ahrens M, Sokolov SV, Wagner P (2019) Word fire statistics. Report No 24. Center of Fire Statistics of CTIF, State Fire Academy of Emercom of Russia
Campbell R (2019) Home electrical fires. National Fire Protection Association (NFPA), Massachusetts
Gaur A et al (2019) Fire sensing technologies: a review. IEEE Sens J 9(9):3191–3202
Celik A (2010) Fast and efficient method for fire detection using image processing. ETRI J 32(6):881–890
Arthur WKK (2018) A study of video fire detection and its application. Ph.D. dissertation, Department of Building Services Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Muhammad K et al (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174–18183
Verstockt S (2011) Multi-modal video analysis for early fire detection. Dissertation, University of Ghent
Alamgir N, Nguyen K, Chandran V, Boles W (2018) Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos. Fire Saf J 102:1–10
Li M, Xu W, Xu K, Fan J, Hou D (2013) Review of fire detection technologies based on video image. J Theor Appl Inf Technol 49(2):700–707
Law CK (2017) Laminar premixed flames. In: Combustion physics. Cambridge University Press, Cambridge, p 300
Hurley MJ, Gottuk DT, Hall JR, Harada K, Kuligowski ED, Puchovsky M, Torero JL, Watts JM, Weiczorek C (eds) (2016) SFPE handbook of fire protection engineering. Springer, New York. https://doi.org/10.1007/978-1-4939-2565-0
Zhiqiang Z, Yongsheng S, Zhifeng G, Sun L (2016) Wildfire smoke detection based on local extremal region segmentation and surveillance. Fire Saf J 85:50–58
Vandecasteele F, Merci B, Verstockt S (2016) Smoke behaviour analysis with multi-view smoke spread data. In: Proceedings of the 14th international interflam conference 1, Royal Holloway Col., University of London, U.K. pp 399–408
Torabnezhad M, Aghagolzadeh A, Seyedarabi H (2013) Visible and IR image fusion algorithm for short range smoke detection. In: Proceedings of the 2013 RSI/ISM international conference on robotics and mechatronics, Tehran, Iran. pp 038–042
Yuan F et al (2015) Real-time image smoke detection using staircase-searching based dual threshold AdaBoost and dynamic analysis. IET Image Process 9(10):849–856
Rong J et al (2013) Fire flame detection based on GICA and target tracking. Opt Laser Technol 47:283–291
Wang S et al (2014) Early smoke detection in video using swaying and diffusion feature. J Intell Fuzzy Syst 26:267–275
Toreyin BU, Dedeoglu Y, Cetin AE (2005) Wavelet based real-time smoke detection in video. In: 2005 13th European signal processing conference Antalya, 2005, pp 1–4
Xuehui W, Xiaobo L, Leung H (2018) A video based fire smoke detection using Robust AdaBoost. Sensors 8:1–22
Gomez-Rodriguez F, Arrue BC, Ollero A (2003) Smoke monitoring and measurement using image processing application to forest fires. In: Proceedings of the SPIE-the international society for optical Eng, Orlando, Florida, United States. pp 404–409
Fujiwara N, Terada K (2004) Extraction of a smoke region using fractal coding. In: International symposium on communications and information technology, Sapporo, Japan, pp 659–662
Chen TH, Yin YH, Huang SF, Ye YT (2006) The smoke detection for early fire-alarming system base on video processing. In: Proceedings of the 2006 international conference on intelligent info hiding and multimedia signal processing, IEEE Computer Society, Pasadena, CA, USA. pp 427–430
Celik T, Ozkaramanh H, Demirel H (2007) Fire and smoke detection without sensors: image processing based approach. In: 2007 15th European signal proceedings of the conference, Poznan, Poland, pp 1794–1798
Xu Z, Xu J (2007) Automatic fire smoke detection based on image visual features. In: 2007 International conference on computational intelligence and security workshops, Heilongjiang, pp 316–319
Yang J, Chen F, Zhang W (2008) Visual-based smoke detection using support vector machine. In: 2008 4th International conference on natural computation, Jinan, pp 301–305
Piccinini P, Calderara S, Cucchiara R (2008) Reliable smoke detection in the domains of image energy and color. In: 2008 15th IEEE international conference on image proceedings, San Diego, CA, pp 1376–1379
Gubbi J, Marusic S, Palaniswami M (2009) Smoke detection in video using wavelets and support vector machines. Fire Saf J 44:1110–1115
Yu C, Zhang Y, Fang J, Wang J (2009) Texture analysis of smoke for real-time fire detection. In: 2009 2nd International workshop on computer science and engineering, Qingdao, pp 511–515
Toreyin BU (2009) Fire detection algorithms using multimodal signal and image analysis. Ph.D. dissertation, Department of Electrical and Electronics Engineering, The Institution of Engineering and Science, Bilkent University
Verstockt S et al (2009) State of the art in vision-based fire and smoke detection. In: Proceedings of the 14th international conference on automatic fire detection, Duisburg, Germany. pp 285–292
Krstinic D, Stipanicev D, Jacovcevic T (2009) Histogram-based smoke segmentation in forest fire detection system. Inf Technol Control 38(3):237–244
Kim D, Wang Y (2009) Smoke detection in video. In: 2009 WRI World congress on computer science and information engineering, Los Angeles, CA, pp 759–763
Han D, Lee B (2009) Flame and smoke detection method for early real-time detection of a tunnel fire. Fire Saf J 44:251–261
Zhou Y, Yi X, Xiaokang Y (2010) Fire surveillance method based on quaternionic wavelet features. In: Proceedings of the international conference on multimedia modeling: advances in multimedia modeling. Springer, Berlin, Heidelberg. vol 5916, pp 477–488
Ma L, Wu K, Zhu L (2010) Fire smoke detection in video images using Kalman filter and Gaussian mixture color model. In: Proceedings of the 2010 international conference on artificial intelligence and computational intelligence part 1, Sanya, China. pp 484–487
Chengjiang L et al (2010) Transmission: a new feature for computer vision based smoke detection. In: Proceedings of the international conference AICI 2010, Sanya, China. pp 389–396
Kwak JY, Ko BC, Nam J (2011) Forest smoke detection using CCD camera and spatial-temporal variation of smoke visual patterns. In: Proceedings of the 2011 8th international conference computer graphics, imaging and visualization, Singapore. pp 141–144
Habiboglu YH, Gunay O, Cetin AE (2011) Real-time wildfire detection using correlation descriptors. In: 2011 19th European signal processing conference, Barcelona, pp 894–898
Yuan F (2011) Video-based smoke detection with histogram sequence of LBP and LBPV pyramids. Fire Saf J 46:132–139
Truong XT, Jong-Myon K (2011) An effective four-stage smoke-detection algorithm using video images for early fire alarm systems. Fire Saf J 46:276–282
Yuan F (2012) A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection. Pattern Recognit 45:4326–4336
Lee CY, Lin CT, Hong CT, Su MT (2012) Smoke detection using spatial and temporal analyses. Int J Innov Comput 8(7):4749–4770
Avgerinakis K, Briassouli A, Kompatsiaris I (2012) Smoke detection using temporal HOGHOF descriptors and energy color statistics from video. In: Proceedings of the international workshop on multi-sensor system and network. Fire detection and management, Antalya, Turkey, pp 1–4
Vidal-Calleja TA, Agammenoni G (2012) Integrated probabilistic generative model for detecting smoke on visual images. In: Proceedings of the 2012 IEEE international conference on robotics and automation, Saint Paul, MN. pp 2183–2188
Tian H, Li W, Wang L, Ogunbona P (2012) A novel video-based smoke detection method using image separation. In: Proceedings of the 2012 IEEE international conference on multimedia and expo, Melbourne, VIC. pp 532–537
Gunay O, Toreyin BU, Kose K, Cetin AE (2012) Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video. IEEE Trans Image Process 21(5):2853–2865
Labati RD, Genovese A, Piuri V, Scotti F (2013) Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation. IEEE Trans Syst Man Cybern Part A Syst Hum 43(4):1003–1012
Yu C, Mei Z, Zhang X (2013) A real-time fire flame and smoke detection algorithm. Procedia Eng 62:891–898
Junzhou C, Yong Y (2013) Early fire detection using HEP and space-time analysis. arXiv preprint arXiv: 1310.1855.
Ho CC (2013) Nighttime fire/smoke detection system based on a support vector machine. Math Probl Eng 2013, Article ID 428545, pp 1–7
Zhao Y, Zhou Z, Xu M (2015) Forest fire smoke detection using spatiotemporal and dynamic texture features. J Electr Comput Eng 2015, Article ID 706187, pp 1–7
Ye W et al (2015) Dynamic texture based smoke detection using surfacelet transform and HMT model. Fire Saf J 73:91–101
Qureshi WS et al (2016) QuickBlaze: early fire detection using a combined video processing approach. Fire Technol 52:1293–1317
Yuanbin W (2016) Smoke recognition based on machine vision. In: Proceedings of the 2016 International symposium on computer, consumer and control (IS3C), Xi’an. pp 668–671
Shiping Y et al (2017) An effective algorithm to detect both smoke and flame using color and wavelet analysis. Pattern Recognit Image Anal 27(1):131–138
Wang S et al (2017) Video smoke detection using shape, color and dynamic features. J Intell Fuzzy Syst 33:305–313
Dimitropoulos K, Barmpoutis P, Grammalidis N (2017) Higher order linear dynamical systems for smoke detection in video surveillance applications. IEEE Trans Circuit Syst Video Technol 27(5):1143–1154
Liu CB, Ahuja N (2004) Vision based fire detection. In: 17th International conference on pattern recognition, Cambridge, pp 134–137
Chen TH, Wu PH, Chiou YC (2004) An early fire-detection method based on image processing. In: 2004 International conference on image processing, Singapore, pp 1707–1710
Horng WB, Peng JW, Chen CY (2005) A new image-based real-time flame detection method using color analysis. In: Proceedings of the 2005 IEEE networking, sensing and control, Tucson, AZ. pp 100–105
Dedeoglu Y, Toreyin BU, Gudukbay U, Cetin AE (2005) Real-time fire and flame detection in video. In: Proceedings of the IEEE international conference on acoustic, speech, and signal processing, Philadelphia, PA. vol 2, pp 669–672
Toreyin BU, Dedeoglu Y, Cetin AE (2005) Flame detection in videos using hidden Markov models. In: IEEE international conference on image processing, Genova, pp 1–4
Marbach G, Loepfe M, Brupbacher T (2006) An image processing technique for fire detection in video images. Fire Saf J 41:285–289
Borges PVK, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuit Syst Video Technol 20(5):721–731
Horng WB, Peng JW (2008) A fast image-based fire flame detection method using color analysis. Tamkang J Sci Eng 11(3):273–285
Zhang Z et al (2008) Contour based forest fire detection using FFT and wavelet. In: 2008 International conference on computer science and software engineering, Hubei, pp 760–763
True N Computer vision based fire detection [Online]. Available: http://pdf.semanticscholar.org. Accessed 25 Oct 2019
Qi X, Ebert J (2009) A computer vision based method for fire detection in color videos. Int J Imaging 2(9):22–34
Gunay O et al (2010) Fire detection in video using LMS based active learning. Fire Technol 46:551–577
Gunay O, Tasdemir K, Toreyin BU, Cetin AE (2009) Video based wildfire detection at night. Fire Saf J 44:860–868
Ko BC, Cheong KH, Nam JY (2009) Fire detection based on vision sensor and support vector machines. Fire Saf J 44:322–329
Zhu T, Jeong-Hyun K, Dong-Joong K (2010) Fire detection based on hidden Markov models. Int Control Autom Syst 8(4):822–830
Chen J, He Y, Wang J (2010) Multi-feature fusion based fast video flame detection. Build Environ 45:1113–1122
Jiang Q, Wang Q (2010) Large space fire image processing of improving Canny edge detector based on adaptive smoothing. In: 2010 International conference on innovative computing and communications and 2010 Asia-Pacific conference on information technology and ocean engineering, Macao, pp 264–267
Ko BC, Cheong KH, Nam JY (2010) Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks. Fire Saf J 45:262–270
Zhou Y, Yi X, Xiaokang Y (2010) Fire surveillance method based on quaternionic wavelet features. In: Proceedings of the 16th international multimedia modeling conference, Chongquing, China. pp 477–488
Yu-Chiang L, Wei-Cheng W (2011) Visual fire detection based on data mining technique. In: 2011 First international conference on robot, vision and signal Processing, Kaohsiung, pp 328–331
Ko BC, Ham SJ, Nam JY (2011) Modeling and formalization of fuzzy finite automata for detection of irregular fire flames. IEEE Trans Circuits Syst Video Technol 21(12):1903–1912
Rossi L, Akhloufi M, Tison Y (2010) On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics. Fire Saf J 46:9–20
Zhao J et al (2011) SVM based forest fire detection using static and dynamic features. Comput Sci Inf Syst 8(3):821–841
Yunyang Y, Shangbing G, Hongyan W, and Zhibo G (2012) Contour extraction of flame for fire detection. Adv Mater Res 383–390:1106–1110
Qiu T, Yan Y, Lu G (2012) An auto-adaptive edge detection algorithm for flame and fire image processing. IEEE Trans Instrum Meas 61(5):1486–1493
Dimitropoulos K, Tsalakanidou F, Grammalidis N (2012) Flame detection for video-based early fire warning systems and 3D visualization of fire propagation. In: 13th IASTED international conference on computer graphics and imaging, Crete, Greece, pp 1–15
Wang W, Zhou H (2012) Fire detection based on flame color and area. In: 2012 IEEE international conference on computer science and automation engineering (CSAE), Zhangjiajie, pp 222–226
Mueller M, Karasev P, Kolesov I, Tannenbaum A (2013) Optical flow estimation for flame detection in videos. IEEE Trans on Image Process 22(7):2786-2797
Wang DC et al (2013) Adaptive flame detection using randomness testing and robust features. Fire Saf J 55:116–125
Lascio RD, Greco A, Saggese A, Vento M (2014) Improving fire detection reliability by a combination of videoanalytics. In: International conference image analysis and recognition, ICIAR 2014, Springer, Cham, pp 477–484
Ko BC, Jung JH, Nam JY (2014) Fire detection and 3D surface reconstruction based on stereoscopic pictures and probabilistic fuzzy logic. Fire Saf J 68:61–70
Schroder T, Kruger K, Kummerlen F (2014) Image processing based deflagration detection using fuzzy logic classification. Fire Saf J 65:1–10
Stadler A, Windisch T, Diepold K (2014) Comparison of intensity flickering features for video based flame detection algorithm. Fire Saf J 66:1–7
Wong AKK, Fong NK (2014) Experimental study of video fire detection and its applications. Procedia Eng 71:316–327
Zhang Z (2014) An improved probabilistic approach for fire detection in videos. Fire Technol 50:745–752
Chino DYT, Avalhais LPS, Rodrigues JFS, Traina AJM (2015) BoWFire: detection of fire in still images by integrating pixel color and texture analysis. In: 2015 28th SIBGRAPI conference on graphics, patterns and images, Salvador, pp 95–102
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(9):1545–1556
Dimitropoulos K, Barmpoutis P, Grammalidis N (2015) Spatio-temporal flame modelling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans Circuits Syst Video Technol 25(2):339–351
Rui C, Zhe-Ming L, Qing-Ge J (2017) Real-time multi-feature based fire flame detection in video. IET Image Process 11(1):31–37
Toulouse T, Rossi L, Celik T, Akhloufi M (2016) Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods. Signal Image Video Processing 10(4):647–654
Phillips-III W, Shah M, Lobo NDV (2002) Flame recognition in video. Pattern Recognit Lett 23(1–3):319–327
Kong SG, Jin D, Li S, Kim HH (2016) Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Saf J 79:37–43
Han XF et al (2017) Video fire detection based on Gaussian mixture model and multi-color features. Signal Image Video Process 11(8):1419–1425
Gong F et al (2019) A real-time fire detection method from video with multifeature fusion. Comput Intell Neurosci 2019, Article ID 1939171, pp 1–17
Arrue BC, Ollero A, Dios JRMD (2000) An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intell Syst Appl 15(3):64–73
Bosch I, Gomez S, Vergara L, Moragues J (2007) Infrared image processing and its application to forest fire surveillance. In: 2007 IEEE conference on advanced video and signal based surveillance, London, pp 283–288
Bosch I, Gomez S, Molina R, Miralles R (2009) Object discrimination by infrared image processing. In: Proceedings of the international work-conference on the interplay between natural and artificial computation, Santiago de Compostela. pp 30–40
Liu Y, Yu C, Zhang Y (2010) Nighttime video smoke detection based on active infrared video image. In: Proceedings of the international conference on electrical and control engineering, Wuhan, China. pp 1359–1362
Verstockt S et al (2010) Video fire detection using non-visible light. In: Proceedings of the 6th International seminar on fire and explosion hazards, Leeds, U.K. pp 549–559
Verstockt S et al (2010) Multi-sensor fire detection by fusing visual and non-visual flame features. In: Proceedings of the 4th International conference ICISP, Trois-Rivieres, QC, Canada. pp 333–341
Verstockt S et al (2010) Hot topics in video fire analysis. Newslett Int Assoc Fire Saf Sci 29:1–5
Verstockt S et al (2011) Future directions for video fire detection. In: Proceedings of the 10th international symposium on fire safety science, College Park, MD, USA. pp 529–542
Dios JRM, Merino L, Caballero F, Ollero A (2011) Automatic forest-fire measuring using ground stations and unmanned aerial systems. Sensors 11:6328–6353
Bosch I, Gomez S, Vergara L (2011) A ground system for early forest fire detection based on infrared signal processing. Int J Remote Sens 32:4857–4870
Bosch I, Serrano A, Vergara L (2013) Multisensor network system for wildfire detection using infrared image processing. Sci World J 2013, Article ID 402196, pp 1–10
Verstockt S et al (2013) A multi-modal video analysis approach for car park fire detection. Fire Saf J 57:44–57
Won-Ho K (2014) DSP embedded early fire detection method using IR thermal video. KSII Trans Internet Inf Syst 8(10):3475–3489
Cun YL, Bengio Y, Hinton G (2015) Deep learning. Nat Int J Sci 521:436–444
Gonzalez RC (2018) Deep convolutional neural networks. IEEE Signal Process Mag 35(6):79–87
Willis S (2017) Stand on the shoulders of giants. IEEE Comput 50(7):99–102
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process 3(2):1–29
Arel I, Rose DC, Karnowski TP (2010) Deep machine learning-a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5(4):13–18
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
Grammalidis N, Dimitropoulos K, Cetin AE. FIRESENSE database of videos for flame and smoke detection [Dataset]. Zenodo. http://doi.org/10.5281/zenodo.836749.
Mivia: Mivia fire detection dataset. http://mivia.unisa.it/. Accessed 18 Oct 2019
RABOT 2012 | Multimedia Lab— UGent. Multimedialab.elis.ugent.be/rabot2012/
Pinto N, Cox DD, DiCarlo JJ (2008) Why is real-world visual object recognition hard? PLoS Comput Biol 4(1):0151–0156
Mopuri KR, Babu RV (2015) Object level deep feature pooling for compact image representation. In: Proceedings of the 2015 IEEE conference on computer vision and pattern recognition workshops, Boston, MA. pp 62–70
BcT. Polednik (2015) Detection of fire in images and videos. Excel@FIT 1–11
Zhang Q, Xu J, Xu L, Guo H (2016) Deep convolutional neural network for forest fire detection. In: Proceedings of the 2016 international forum on management, education and information technology application, Atlantis Press. pp 568–575
Kim S et al (2016) Forest fire monitoring system based on aerial image. In: 2016 3rd International conference on information and communication technology for disaster management, Vienna, pp 1–6
Gonzalez A et al (2017) Accurate fire detection through fully convolutional neural network. In: 7th Latin American conference on networked and electronic media, Valparaiso, pp 1–6
Huttner V, Steffens CR, Silva da Costa Botelho S (2017) First response fire combat: deep learning based visible fire detection. In: 2017 Latin American robotics symposium and 2017 Brazilian symposium on robotics, Curitiba, pp 1–6
Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30–42
Shen D, Chen X, Nguyen M, Yan WQ (2018) Flame detection using deep learning. In: Proceedings of the 2018 4th international conference on control automation and robotics, Auckland. pp 416–420
Zhao Y, Ma J, Li X, Zhang J (2018) Saliency detection and deep learning-based wildfire identification in UAV imagery. Sensors 18:1–19
Aslan S, Gudukbay U, Toreyin BU, Cetin AE (2019) Deep convolutional generative adversarial networks based flame detection in Video. arXiv preprint arXiv: 1902.01824.
Muhammad K et al (2019) Efficient deep CNN based fire detection and localization in video surveillance applications. IEEE Trans Syst Man Cybern Syst 49(7):1419–1434
Kim B, Lee J (2019) A video-based fire detection using deep learning models. Appl Sci 9(2862):1–19
Yin Z et al (2017) A deep normalization and convolutional neural network for image smoke detection. IEEE Access 5:18429–18438
Zeng J et al (2018) An improved object detection method based on deep convolution neural network for smoke detection. In: IEEE international conference on machine learning and cybernetics, Chengdu, China, pp 184–189
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
Xu G et al (2019) Video smoke detection based on deep saliency Network. Fire Saf J 105:277–285
Wu X, Lu X, Leung H (2014) An adaptive threshold deep learning method for fire and smoke detection. In: Proceedings of the IEEE international conference on systems, man and cybernetics, Banff, Canada. pp 1954–1959
Zhang Q et al (2018) Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. In: 2017 8th International conference on fire science and fire protection engineering, pp 441–446
Namozov A, Cho YI (2018) An efficient deep learning algorithm for fire and smoke detection with limited data. Adv Electr Comput Eng 18(4):121–128
Maksymiv O, Rak T, Menshikova O (2016) Deep convolutional network for detecting probable emergency situations. In: IEEE 1st international conference on data stream mining and processing, Lviv, Ukraine, pp 199–202
Hu Y, Lu X (2018) Real-time video fire smoke detection by utilizing spatial-temporal ConvNet features. Multimed Tools Appl 77:29283–29301
Gaohua L, Yongming Z, Gao X, Qixing Z (2019) Smoke detection on video sequences using 3D convolutional neural networks. Fire Technol 55:1827–1847
Tao C, Zhang J, Wang P (2016) Smoke detection based on convolutional neural networks. In: International conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII), Wuhan, pp 150–153
Luo Y, Zhao L, Liu P, Huang D (2018) Fire smoke detection algorithm based on motion characteristics and convolutional neural networks. Multimed Tools Appl 77(12):15075–15092
Frizzi S et al (2016) Convolutional neural network for video fire and smoke detection. In: Proceedings of the 42nd Annual conference of the IEEE industrial electronics society, Florence. pp 877–882
Jadon A et al (2019) FireNet: a specialized lightweight fire and smoke detection model for real-time IoT applications. arXiv preprint arXiv: 1905.11922.
Choo J, Liu S (2018) Visual analytics for explainable deep learning. IEEE Comput Graph Appl 38(4):84–92
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision, Springer, Zurich, Switzerland. pp 818–833
Cetin AE et al (2013) Video fire detection-review. Digit Signal Process 23:1827–1843
Ojo JA, Oladosu JA (2014) Video-based smoke detection algorithms: a chronological survey. Comput Eng Intell Syst 5(7):38–50
Alkhatib AAA (2014) A review on forest fire detection techniques. Int J Distrib Sens Netw 2014, Article ID 97368, pp 1–12
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Gaur, A., Singh, A., Kumar, A. et al. Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review. Fire Technol 56, 1943–1980 (2020). https://doi.org/10.1007/s10694-020-00986-y
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DOI: https://doi.org/10.1007/s10694-020-00986-y