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Deep Learning for Fire and Smoke Detection in Outdoor Spaces

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Smart Modelling for Engineering Systems

Abstract

Recent investigations in deep learning provide a new approach for fire and smoke detection in outdoor spaces. Fire and smoke detection is an additional function in smart urban surveillance, as well as, in wildlife monitoring using stationary cameras or UAV cameras. Smoke detection plays an important role in a fire alarm. The algorithms based on the traditional machine learning techniques show high values of errors that cause additional economic costs. Deep learning techniques solve this problem partly but raise new challenges. In this chapter, we analyze deep learning models applicable for this task and present a Weaved Recurrent Single Shot Detector (WRSSD) for early smoke detection with acceptable error ratios.

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References

  1. Lin, Z., Chen, F., Niu, Z., Li, B., Yu, B., Jia, H., Zhang, M.: An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sens. Environ. 211, 376–387 (2018)

    Article  Google Scholar 

  2. Celik, T.: Fast and efficient method for fire detection using image processing. ETRI J. 32(6), 881–890 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Khatami, A., Mirghasemi, S., Khosravi, A., Lim, C.P., Nahavandi, S.: A new PSO-based approach to fire flame detection using K-Medoids clustering. Expert Syst. Appl. 68, 69–80 (2017)

    Article  Google Scholar 

  5. Töreyin, B.U., Dedeoglu, Y., Güdükbay, U., Cetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49–58 (2006)

    Article  Google Scholar 

  6. Truong, T.X., Kim, J.M.: Fire flame detection in video sequences using multi-stage pattern recognition techniques. Eng. Appl. Artif. Intell. 25(7), 1365–1372 (2012)

    Article  Google Scholar 

  7. Ho, C.-C.: Nighttime fire/smoke detection system based on a support vector machine. Math. Probl. Eng. ID 428545.1–ID 428545.7 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  9. Elmas, C., Sönmez, Y.: A data fusion framework with novel hybrid algorithm for multi-agent decision support system for forest fire. Expert Syst. Appl. 38(8), 9225–9236 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Sharma J., Granmo OC., Goodwin M., Fidje J.T.: Deep convolutional neural networks for fire detection in images. In: Boracchi G., Iliadis L., Jayne C., Likas A. (eds.) Engineering Applications of Neural Networks. EANN 2017. CCIS, vol. 744, pp. 183–193. Springer, Cham (2017)

    Google Scholar 

  12. Sousa, M.J., Moutinho, A., Almeida, M.: Wildfire detection using transfer learning on augmented datasets. Expert Syst. Appl. 142, 112975.1–112975.14 (2020)

    Google Scholar 

  13. Wu, H., Wu, D., Jinsong Zhao, J.: An intelligent fire detection approach through cameras based on computer vision methods. Process Saf. Environ. Prot. 127, 245–256 (2019)

    Article  Google Scholar 

  14. Li, P., Zhao, W.: Image fire detection algorithms based on convolutional neural networks. Case Stud. Thermal Eng. (2020) (in print)

    Google Scholar 

  15. Calderara, S., Piccinini, P., Cucchiara, R.: Vision based smoke detection system using image energy and color information. Mach. Vis. Appl. 22, 705–719 (2011)

    Article  Google Scholar 

  16. Favorskaya, M., Pyataeva, A., Popov, A.: Spatio-temporal smoke clustering in outdoor scenes based on boosted random forests. Procedia Comput. Sci. 96, 762–771 (2016)

    Article  Google Scholar 

  17. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Contour based smoke detection in video using wavelets. In: 14th European Signal Processing Conference, Florence, Italy, pp. 1–5 (2006)

    Google Scholar 

  18. Lin, G., Zhang, Y., Zhang, Q., Zhang, J., Jia, Y., Xu, G., Wang, J.: Smoke detection in video sequences based on dynamic texture using volume local binary patterns. KSII Trans. Internet Inf. Syst. 11(11), 5522–5536 (2017)

    Google Scholar 

  19. Yin, Z., Wan, B., Yuan, F., Xia, X., Shi, J.: A deep normalization and convolutional neural network for image smoke detection. IEEE Access 5, 18429–18438 (2017)

    Article  Google Scholar 

  20. Li, T., Zhao, E., Zhang, J., Hu, C.: Detection of wildfire smoke images based on a densely dilated convolutional network. Electronics 8(10), 1131.1–1131.12 (2019)

    Google Scholar 

  21. Yin, M., Lang, C., Li, Z., Feng, S., Wang, T.: Recurrent convolutional network for video-based smoke detection. Multimedia Tools Appl. 8, 1–20 (2018)

    Google Scholar 

  22. Jia, Y., Du, H., Wang, H., Yu, R., Fan, L., Xu, G., Zhang, Q.: Automatic early smoke segmentation based on conditional generative adversarial networks. Optik Int. J. Light Electron. Opt. 193, 162879.1–162879.13 (2019)

    Google Scholar 

  23. Xu, G., Zhang, Y., Zhang, Q., Lin, G., Wang, Z., Jia, Y., Wang, J.: Video smoke detection based on deep saliency network. Fire Saf. J. 105, 277–285 (2019)

    Article  Google Scholar 

  24. Peng, Y., Wang, Y.: Real-time forest smoke detection using hand-designed features and deep learning. Comput. Electron. Agric. 167, 105029.1–105029.18 (2019)

    Google Scholar 

  25. Chen, T.-H., Wu, P.-H., Chiou, Y.-C.: An early fire-detection method based on image processing. In: 2004 International Conference on Image Processing, vol. 3, pp. 1707–1710 IEEE (2004)

    Google Scholar 

  26. Namozov, A., Cho, Y.I.: An efficient deep learning algorithm for fire and smoke detection with limited data. Adv. Electr. Comput. Eng. 18(4), 121–128 (2018)

    Article  Google Scholar 

  27. Zhang, Q., Lin, G., Zhang, Y., Xu, G., Wang, J.: Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Procedia Eng. 211, 441–446 (2018)

    Article  Google Scholar 

  28. Jadon, A., Omama, M., Varshney, A., Ansari, M.S., Sharma, R.: FireNet: a specialized lightweight fire and smoke detection model for real-time IoT applications. CoRR ArXiv Preprint, arXiv:1905.11922v2 [cs.CV] (2019)

  29. Favorskaya, M., Levtin, K.: Early video-based smoke detection in outdoor spaces by spatio-temporal clustering. Int. J. Reason.-Based Intell. Syst. 5(2), 133–144 (2013)

    Google Scholar 

  30. Favorskaya, M., Pyataeva, A., Popov, A.: Verification of smoke detection in video sequences based on spatio-temporal local binary patterns. Procedia Comput. Sci. 60, 671–680 (2015)

    Article  Google Scholar 

  31. Tan, K., Wang, D.: A convolutional recurrent neural network for real-time speech enhancement. Interspeech 1405, 3229–3233 (2018)

    Article  Google Scholar 

  32. Chen, G., Ye, D., Xing, Z-C., Chen, J., Cambria, E.: Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: International Joint Conference on Neural Networks, pp. 2377–2383 (2017)

    Google Scholar 

  33. Zuo, H., Fan, H., Blasch, E., Ling, H.: Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24(3), 289–293 (2017)

    Article  Google Scholar 

  34. Ordóñez, F.J., Roggen D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 115.1–115.25 (2016)

    Google Scholar 

  35. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. CoRR arXiv preprint, arXiv:1704.04861 (2017)

  36. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision—ECCV 2016, LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016)

    Chapter  Google Scholar 

  37. Smoke Detection Dataset. https://mivia.unisa.it/datasets/video-analysis-datasets/smoke-detection-dataset/. Last accessed 03 May 202

  38. Database of Bilkent University. https://signal.ee.bilkent.edu.tr/VisiFire/Demo/SmokeClips/. Last accessed 03 May 2020

  39. DynTex. https://projects.cwi.nl/dyntex/. Last accessed 03 May 2020

  40. Favorskaya, M., Pakhirka, A.: Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Comput. Sci. 159, 933–942 (2019)

    Article  Google Scholar 

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Correspondence to Margarita N. Favorskaya .

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Favorskaya, M.N., Jain, L.C. (2021). Deep Learning for Fire and Smoke Detection in Outdoor Spaces. In: Favorskaya, M.N., Favorskaya, A.V., Petrov, I.B., Jain, L.C. (eds) Smart Modelling for Engineering Systems. Smart Innovation, Systems and Technologies, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-33-4619-2_15

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