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Convolutional neural network based early fire detection

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Abstract

The detection of manmade disasters particularly fire is valuable because it causes many damages in terms of human lives. Research on fire detection using wireless sensor network and video-based methods is a very hot research topic. However, the WSN based detection model need fire happens and a lot of smoke and fire for detection. Similarly, video-based models also have some drawbacks because conventional algorithms need feature vectors and high rule-based models for detection. In this paper, we proposed a fire detection method which is based on powerful machine learning and deep learning algorithms. We used both sensors data as well as images data for fire prevention. Our proposed model has three main deep neural networks i.e. a hybrid model which consists of Adaboost and many MLP neural networks, Adaboost-LBP model and finally convolutional neural network. We used Adaboost-MLP model to predict the fire. After the prediction, we proposed two neural networks i.e. Adaboost-LBP model and convolutional neural network for detection of fire using the videos and images taken from the cameras installed for the surveillance. Adaboost-LBP model is to generate the ROIs from the image where emergencies exist Our proposed model results are quite good, and the accuracy is almost 99%. The false alarming rate is very low and can be reduced more using further training.

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References

  1. Anwar S, Hwang K, Sung W (2015) Fixed point optimization of deep convolutional neural networks for object recognition, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1131-1135

  2. Bhattacharjee S, Roy P, Ghosh S, Misra S, Obaidat MS (2012) Wireless sensor network-based fire detection, alarming, monitoring and prevention system for Bord-and-Pillar coal mines. J Syst Softw 85(3):571–581

    Article  Google Scholar 

  3. 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 

  4. Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24:5017–5032

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen TH, Wu PH, Chiou YC (2004) An early fire-detection method based on image processing. InImage Processing, 2004. ICIP'04. 2004 International Conference on 2004 Oct 24 (Vol. 3, pp. 1707-1710). IEEE

  6. Chen T-H, Wu P-H, Chiou Y-C (2004) An early fire-detection method based on im- age processing, in: Proceedings of International Conference on Image Process- ing, ICIP’04, 2004, pp. 1707–1710

  7. Chino DY, Avalhais LP, Rodrigues JF, Traina AJ (2015) BoWFire: detection of fire in still images by integrating pixel colour and texture analysis, Proceedings of the 28th 2015 Conference on Graphics, Patterns and Images, SIBGRAPI pp. 95-102

  8. Foggia P, Saggese A, Vento M (2015) Real-time fire detection for video-surveillance applications using a combination of experts based on colour, shape, and motion. IEEE Trans Circuits Syst Video Technol 25:1545–1556

    Article  Google Scholar 

  9. Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285

    Article  MathSciNet  MATH  Google Scholar 

  10. Guo L, Ge P-S, Zhang M-H, Li L-H, Zhao Y-B (2012) Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine. Expert Syst Appl 39(4):4274–4286

    Article  Google Scholar 

  11. Habiboğlu YH, Günay O, Çetin AE (2012) Covariance matrix-based fire and flame detection method in video. Mach Vis Appl 23(6):1103–1113

    Article  Google Scholar 

  12. Han D, Lee B (2006) Development of early tunnel fire detection algorithm using the image processing, in: Proceedings of International Symposium on Visual Com- puting, pp. 39–48

  13. https://archive.ics.uci.edu/ml/datasets/forest+fires. “Forest fire dataset” 2008-02-29

  14. Jiang B, Yang J, Lv Z, Tian K, Meng Q, Yan Y (2017) Internet cross-media retrieval based on deep learning. J Vis Commun Image Represent 48:356–366

    Article  Google Scholar 

  15. Kantorov V, Oquab M, Cho M, Laptev I (2016) ContextLocNet: context-aware deep network models for weakly supervised localization. Proceedings of European Conference on Computer Vision, pp. 350-365

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. Adv Neural Inf Proces Syst 25:1097–1105

    Google Scholar 

  17. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  18. Li W, Zhao R, Xiao T, Wang X (2014) Deepreid: Deep filter pairing neural network for person reidentification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 152-159

  19. Liu CB, Ahuja N (2004) Vision based fire detection. In Pattern Recognition. ICPR 2004. Proceedings of the 17th International Conference on 2004 Aug 23 (Vol. 4, pp. 134-137). IEEE

  20. Liu C-B, Ahuja N (2004) Vision based fire detection, in: Proceedings of the 17th In-ternational Conference on Pattern Recognition, ICPR 2004, pp. 134–137

  21. 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  Google Scholar 

  22. Luo P, Tian Y, Wang X, Tang X (2014) Switchable deep network for pedestrian detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 899-906

  23. Mueller M, Karasev P, Kolesov I, Tannenbaum A (2013) Optical flow estimation for flame detection in videos. IEEE Trans Image Process 22:2786–2797

    Article  Google Scholar 

  24. Ojala T, Pietikäinen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582 - 585

  25. Ojala T, Pietikäinen M, Harwood D (1996) A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recogn 29:51–59

    Article  Google Scholar 

  26. Phillips Iii W, Shah M, Vitoria Lobo Nd (2002) Flame recognition in video. Pattern Recogn Lett 23:319–327

    Article  MATH  Google Scholar 

  27. Rafei M, Sorkhabi SE, Mosavi MR (2014) Multi-objective optimization by means of multi-dimensional mlp neural networks. Neural Netw World 24(1):31–56

    Article  Google Scholar 

  28. Saeed F, Paul A, Rehman A, Hong WH, Seo H (2018) IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety. J Sens Actuator Netw 7(1):11

    Article  Google Scholar 

  29. Silva C, Bouwmans T, Frelicot C (2015) An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos, VISAPP 2015, Berlin, Germany

  30. 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 

  31. Tan W, Wang Q, Huang H, Guo Y, Zhan G (2007) Mine Fire Detection System Based on Wireless Sensor Networks. In Proceedings of the Conference on Information Acquisition (ICIA’07), Seogwipo-si, Korea, 8–11 July

  32. Tao C, Zhang J, Wang P (2016) Smoke detection based on deep convolutional neural networks. In2016 International conference on industrial informatics-computing technology, intelligent technology, industrial information integration (ICIICII) (pp. 150-153). IEEE

  33. Yang J, Jiang B, Li B, Tian K, Lv Z (2017) A fast image retrieval method designed for network big data. IEEE Trans Ind Inf 13(5):2350–2359

    Article  Google Scholar 

  34. Yu L, Wang N, Meng X (2005) Real-time forest fire detection with wireless sensor networks. InProceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing. 2005 Sep 23 (Vol. 2, pp. 1214-1217). IEEE

  35. Yuan F, Shi J, Xia X, Fang Y, Fang Z, Mei T (2016) High-order local ternary patterns with locality preserving projection for smoke detection and image classification. Inf Sci 372:225–240

    Article  Google Scholar 

  36. Zhang J, Li W, Han N, Kan J (2008) Forest fire detection system based on a ZigBee wireless sensor network. Front For China 3(3):369–374

    Article  Google Scholar 

  37. Zhang Z, Zhao J, Zhang D, Qu C, Ke Y, Cai B (2008) Contour based forest fire de-tection using FFT and wavelet, in: Proceedings of International Conference on Computer Science and Software Engineering, pp. 760–763

  38. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S et al (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–224

    Article  Google Scholar 

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Acknowledgements

This study was also supported by the national research foundation of Korea (NRF) grant funded by the Korean government (NRF-2017r1c1b5017464).

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Correspondence to Anand Paul.

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Saeed, F., Paul, A., Karthigaikumar, P. et al. Convolutional neural network based early fire detection. Multimed Tools Appl 79, 9083–9099 (2020). https://doi.org/10.1007/s11042-019-07785-w

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