Abstract
At present, the method of detecting fire by convolutional neural network only uses flame or smoke as an indicator of fire occurrence, and such a method is somewhat limited. This article also detects flames and smoke so that it can be alarmed only when smoke or flame is detected. When the smoke and flame are detected at the same time, the credibility of the alarm can be improved. Our experiments show that the proposed network achieves excellent accuracy and speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lin, M.X., Chen, W.L., Liu, B.S., et al.: An intelligent fire-detection method based on image processing. Adv. Eng. Forum 2–3, 172–175 (2011)
Marbach, G., Loepfe, M., Brupbacher, T.: An image processing technique for fire detection in video images. Fire Saf. J. 41(4), 285–289 (2006)
Çelik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009)
Borges, P.V.K., Izquierdo, E.: A probabilistic approach for vision-based fire detection in videos. IEEE Trans. Circuits Syst. Video Technol. 20(5), 721–731 (2010)
Rafiee, A., Tavakoli, R., Dianat, R., et al.: Fire and smoke detection using wavelet analysis and disorder characteristics. In: International Conference on Computer Research & Development, IEEE (2011)
Habiboğlu, Y.H., Günay, O., Çetin, A.E.: Covariance matrix-based fire and flame detection method in video. Mach. Vis. Appl. 23(6), 1103–1113 (2012)
Frizzi, S., Kaabi, R., Bouchouicha, M., et al.: [IEEE IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society - Florence, Italy (2016.10.23-2016.10.26)] IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society - Convolutional neural network for video fire and smoke detection. In: Conference of the IEEE Industrial Electronics Society, IEEE, pp. 877–882 (2016)
Lecun, Y.L., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Tao, C., Zhang, J., Wang, P.: Smoke detection based on deep convolutional neural networks. In: International Conference on Industrial Informatics-computing Technology, IEEE (2017)
Tomas Polednik, Bc.: Detection of fire in images and video using CNN. Excel@FIT (2015)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Xu, Z., et al.: Towards End-to-end license plate detection and recognition: a large dataset and baseline. In: 15th European Conference, Munich, Germany, 8–14 September 2018, Proceedings, Part XIII (2018)
Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp. 577–585 (2015)
Zhou, B., Khosla, A., Lapedriza, A., et al.: Object Detectors Emerge in Deep Scene CNNs. Computer Science (2014)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Li, B., Peng, X., Wang, Z., et al.: AOD-Net: all-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
Ko, B.C., Ham, S.J., Nam, J.Y.: Modeling and formalization of fuzzy finite automata for detection of irregular fire flames. IEEE Trans. Circuits Syst. Video Technol. 21(12), 1903–1912 (2011)
Girshick, R.: Fast R-CNN. Computer Science (2015)
Liu, W., Anguelov, D., Erhan, D., et al.: SSD: single shot multibox detector. In: European Conference on Computer Vision (2016)
Li, H., Wang, P., Shen, C.: Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Trans. Intell. Transp. Syst. PP(99) (2017)
Acknowledgment
This work was supported National Natural Science Foundation of China (61876167 and U1509207).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Pan, Z., Zhang, Z., Song, H., Zhang, S., Zhang, J. (2019). Deep Learning Based Fire Detection System for Surveillance Videos. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11741. Springer, Cham. https://doi.org/10.1007/978-3-030-27532-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-27532-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27531-0
Online ISBN: 978-3-030-27532-7
eBook Packages: Computer ScienceComputer Science (R0)