Skip to main content
SpringerLink
Log in

Fire/Flame Detection with Attention-Based Deep Semantic Segmentation

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

Fire/flame detection from images or videos is very important for early fire warning systems. In this way, fires can be intervened early and extinguished before they grow. Recently, many studies have been published on early fire warning systems based on image processing and machine learning. These studies are generally color space-based image segmentation applications. The given images are first transferred to another color space, and the fire/flame regions are determined by using color segmentation. In this study, a segmentation technique using deep network architecture for fire/flame detection is presented. The proposed method is a segmentation network structure in which the attention gate module is integrated. In the presented method, the success of the deep network architecture is evaluated by using the dice, Tversky, and focal Tversky loss functions. A data set containing 500 images was used for experimental studies, with the fivefold cross-validation criterion, and the success achieved was presented depending on the mean dice and Jaccard similarity criteria. The calculated results were compared with some studies in the literature. The comparison results were shown that the presented technique produced more successful results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

AGM:

Attention gate module

CNN:

Convolutional neural network

DL:

Dice loss

DSC:

Dice similarity coefficient

FTL:

Focal Tversky loss

JSC:

Jaccard similarity coefficient

ROI:

Region of interest

SegNet:

Segmentation network

TL:

Tversky loss

TS:

Tversky similarity

\({b}_{g}{ , b}_{\Psi }\) :

Deviation terms

\({g}_{i}\) :

Gate signal vector collected from a large scale

i :

Pixel index

N :

Total pixels

\({x}_{i}\) :

Feature map of layer output \({i}_{th}\)

\({W}_{g}\) :

Linear transformations using the 1 × 1 × 1-dimensional convolution operator

\({\sigma }_{1}\) :

ReLu function

\({\sigma }_{2}\) :

Sigmoid function

\(\Psi\) :

Linear transformations using the 1 × 1 × 1-dimensional convolution operator

References

  • Abraham N, Khan NM (2019) A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In: IEEE international symposium on biomedical imaging, Venice-Italy, pp. 683–687, 8–11 April 2019

  • Ahuja N (2004) Vision based fire detection. In: 17th international conference on pattern recognition, pp. 134–137, 26 August 2004

  • Altuntas F (2021) Classification, clustering and segmentation of braın mr ımages by ıntellıgent methods, Master’s thesis, Kocaeli University, Graduate School of Natural and Applied Sciences, 2021

  • Arpaci SA, Varli S (2021) EncU-Net: a modified u-net for dermoscopic image segmentation. In: 29th signal processing and communications applications conference (SIU), Istanbul-Turkiye, pp.1–4, 9–11 June 2021

  • Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495

    Article  PubMed  Google Scholar 

  • Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. (2017) Human-level CMR image analysis with deep fully convolutional networks. https://spiral.imperial.ac.uk/handle/10044/1/54263. Yayın tarihi Ekim 25, 2017. Accessed on 11 Ocak 2021

  • Binti Zaidi NI, Binti Lokman NAA, Bin Daud MR, Achmad H, Chia KA (2015) Fire recognition using RGB and YCbCr color space. ARPN J Eng Appl Sci 10(21):9786–9790

    Google Scholar 

  • Budak U, Comert Z, Rashid ZN, Sengur A, Cibuk M (2019) Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput 85:105765

    Article  Google Scholar 

  • Budak U, Guo Y, Tanyildizi E, Sengur A (2020a) Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. Med Hypotheses 134:109431

    Article  PubMed  Google Scholar 

  • Budak U, Comert Z, Cibuk M, Sengur A (2020b) DCCMED-Net: densely connected and concatenated multi encoder-decoder CNNs for retinal vessel extraction from fundus images. Med Hypotheses 134:109426

    Article  PubMed  Google Scholar 

  • Budak U, Cibuk M, Comert Z, Sengur A (2021) Efficient COVID-19 segmentation from CT slices exploiting semantic segmentation with integrated attention mechanism. J Digit Imaging 34(2):263–272

    Article  PubMed  PubMed Central  Google Scholar 

  • Celik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44(2):147–158

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Cetin AE, Dimitropoulos K, Gouverneur B, Grammalidis N, Gunay O, Habiboglu YH, Verstockt S (2013) Video fire detection–review. Digital Signal Process 23(6):1827–1843

    Article  Google Scholar 

  • Chen J, He Y, Wang J (2010) Multi-feature fusion based fast video flame detection. Build Environ 45(5):1113–1122

    Article  Google Scholar 

  • Chi R, Lu ZM, Ji QG (2016) Real-time multi-feature based fire flame detection in video. IET Image Proc 11(1):31–37

    Article  Google Scholar 

  • Corsican Fire Database. https://cfdb.univ-corse.fr/index.php?newlang=english&menu=1. Yayın tarihi Aralık 15, 2020. Accessed on 15 Aralık 2020

  • Deniz E, Sengur A, Kadiroglu Z, Guo Y, Bajaj V, Budak U (2018) Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst 6(1):18

    Article  PubMed  PubMed Central  Google Scholar 

  • Erden F, Toreyin BU, Soyer EB, Inac I, Gunay O, Kose K, Cetin AE (2012) Wavelet based flame detection using differential PIR sensors. In: IEEE 20th signal processing and communications applications conference, pp. 1–4, 18–20 April 2012

  • Fazekas S, Chetverikov D (2007) Analysis and performance evaluation of optical flow features for dynamic texture recognition. Signal Process Image Commun 22(7–8):680–691

    Article  Google Scholar 

  • Fazekas S, Amiaz T, Chetverikov D, Kiryati N (2009) Dynamic texture detection based on motion analysis. Int J Comput Vision 82(1):48

    Article  Google Scholar 

  • Garcia-Jimenez S, Jurio A, Pagola M, De Miguel L, Barrenechea E, Bustince H (2017) Forest fire detection: a fuzzy system approach based on overlap indices. Appl Soft Comput 52:834–842

    Article  Google Scholar 

  • Guldemir NH, Alkan A (2021) Classification of optical coherence tomography images via deep learning. Firat Univ J Eng Sci 33(2):607–615

    Google Scholar 

  • Guo Y, Budak U, Sengur A (2018a) A novel retinal vessel detection approach based on multiple deep convolution neural networks. Comput Methods Programs Biomed 167:43–48

    Article  PubMed  Google Scholar 

  • Guo Y, Budak U, Vespa LJ, Khorasani E, Sengur A (2018b) A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy. Measurement 125:586–591

    Article  ADS  Google Scholar 

  • Khan F, Xu Z, Sun J et al (2022) Recent advances in sensors for fire detection. Sensors 22(9):3310

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • Ko B, Cheong KH, Nam JY (2010) Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian networks. Fire Saf J 45(4):262–270

    Article  Google Scholar 

  • Kong SG, Jin D, Li S, Kim H (2016) Fast fire flame detection in surveillance video using logistic regression and temporal smoothing. Fire Saf J 79:37–43

    Article  Google Scholar 

  • Kosan MA, Coskun A, Karacan H (2019) Entropy in artificial intelligence methods. J Inf Syst Manag Res 1(1):15–22

    Google Scholar 

  • Lee CY, Xie S., Gallagher P, Zhang Z, Tu Z, Deeply-supervised nets. In: 18th international conference on artificial intelligence and statistics, California-USA, pp. 562–570, 9–12 May 2015

  • Li XB, Hua Y, Xia N (2013) Fire detecting technology based on dynamic textures. Procedia Eng 52:186–195

    Article  Google Scholar 

  • Li R, Li M, Li J, Zhou Y (2019) Connection sensitive attention u-net for accurate retinal vessel segmentation. https://arxiv.org/abs/1903.05558. Yayın tarihi Nisan 23, 2019. Accessed on 25 Ocak 2021

  • Lloret J, Garcia M, Bri D, Sendra S (2009) A wireless sensor network deployment for rural and forest fire detection and verification. Sensors 9(11):8722–8747

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Muhammad K, Ahmad J, Baik SW (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30–42

    Article  Google Scholar 

  • Mulla MZ (2021) Cost, activation, loss function, Neural Network, Deep Learning. What are these? Medium, https://medium.com/@zeeshanmulla/cost-activation-lossfunction-neural-network-deep-learning-what-are-these-91167825a4de. Accessed on 10 Ocak 2021

  • Nakau K, Fukuda M, Kushida K, Hayasaka H, Kimura K, Tani H (2006) Forest fire detection based on MODIS satellite imagery and comparison of NOAA satellite imagery with fire fighters’ information, IARC/JAXA terrestrial team workshop, pp. 18–23

  • Patel P, Tiwari S (2012) Flame detection using image processing techniques. Int J Comput Appl 58(18):1–4

    Google Scholar 

  • Qureshi WS, Ekpanyapong M, Dailey MN et al (2016) QuickBlaze: early fire detection using a combined video processing approach. Fire Technol 52:1293–1317

    Article  Google Scholar 

  • Rinsurongkawong S, Ekpanyapong M, Dailey MN, Fire detection for early fire alarm based on optical flow video processing. In: 9th international conference on electrical engineering/electronics, computer, telecommunications and information technology, pp. 1–4, 16–18 May 2012

  • Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention, pp. 234–241, 18 November 2015

  • Saeed F, Paul A, Karthigaikumar P et al (2020) Convolutional neural network based early fire detection. Multimed Tools Appl 79:9083–9099

    Article  Google Scholar 

  • Sengur D, Siuly S (2020) Efficient approach for EEG-based emotion recognition. Electron Lett 56(25):1361–1364

    Article  ADS  Google Scholar 

  • Sengur D, Turhan M (2018) Prediction of the action identification levels of teachers based on organizational commitment and job satisfaction by using k-nearest neighbors method. Turkish J Sci Technol 13(2):61–68

    Google Scholar 

  • ShadabDastgeer IK, Singh SK, Ali I (2016) Fire detection using image processing based on color analysis. Int Res J Eng Technol 3:1–6

    Google Scholar 

  • Solórzano A, Eichmann J, Fernández L et al (2022) Early fire detection based on gas sensor arrays: multivariate calibration and validation. Sens Actuators B Chem 352:130961

    Article  Google Scholar 

  • Sommers WT, Loehman RA, Hardy CC (2014) Wildland fire emissions, carbon, and climate: science overview and knowledge needs. For Ecol Manag 317:1–8

    Article  Google Scholar 

  • Son B, Her YS, Kim JG (2006) A design and implementation of forest-fires surveillance system based on wireless sensor networks for South Korea mountains. Int J Comput Sci Netw Secur 6(9):124–130

    Google Scholar 

  • Stadler A, Windisch T, Diepold K (2014) Comparison of intensity flickering features for video based flame detection algorithms. Fire Saf J 66:1–7

    Article  Google Scholar 

  • Tarsky A (1977) Features of similarity. Psychol Rev 84:327–352

    Article  Google Scholar 

  • Thada V, Jaglan V (2013) Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm. Int J Innov Eng Technol 2(4):202–205

    Google Scholar 

  • Toptas B, Hanbay D (2020) A new artificial bee colony algorithm-based color space for fire/flame detection. Soft Comput 24(14):10481–10492

    Article  Google Scholar 

  • Toreyin BU, Dedeoglu Y, Gudukbay U, Cetin AE (2006) Computer vision based method for real-time fire and flame detection. Pattern Recognit Lett 27(1):49–58

    Article  ADS  Google Scholar 

  • Toreyin BU, Dedeoglu Y, Cetin AE (2005) Flame detection in video using hidden Markov models. In: IEEE international conference on image processing, Genova-Italy, II-1230, 14 September 2005

  • Wang L, Ye M, Ding J, Zhu Y (2011) Hybrid fire detection using hidden Markov model and luminance map. Comput Electr Eng 37(6):905–915

    Article  Google Scholar 

  • Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D (2013) Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans Med Imaging 32(9):1723–1730

    Article  PubMed  Google Scholar 

  • 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 Saf J 73:91–101

    Article  Google Scholar 

  • Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP (2018) Deep Learning in Neuroradiology. Am J Neuroradiol 39(10):1776–1784

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhao Y, Tang G, Xu M (2015) Hierarchical detection of wildfire flame video from pixel level to semantic level. Expert Syst Appl 42(8):4097–4104

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Professor Dr. Abdulkadir Sengur and Associate Professor Dr. Umit Budak for their suggestions regarding the network structure used. This article was produced from Anıl Aliser’s master’s thesis.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Graduate School of Natural and Applied Sciences, University of Firat, Elazig, Turkey

    Anil Aliser

  2. Electrical-Electronics Engineering Department, Faculty of Technology, University of Firat, Elazig, Turkey

    Zeynep Bala Duranay

Authors
  1. Anil Aliser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zeynep Bala Duranay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeynep Bala Duranay.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aliser, A., Duranay, Z.B. Fire/Flame Detection with Attention-Based Deep Semantic Segmentation. Iran J Sci Technol Trans Electr Eng (2024). https://doi.org/10.1007/s40998-024-00697-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40998-024-00697-y

Keywords

Search

Navigation