Abstract
Ecological problems and pollution problems must be faced and solved in the sustainable development of a country. With the continuous development of image analysis technology, it is a good choice to use machine to automatically judge the external environment. In order to solve the problem of smoke extraction and exhaust monitoring, we need the applicable database. Considering the number of databases that can be used to detect smoke is small and these databases have fewer types of pictures, we subdivide the smoke detection database and get a new database for smoke and smoke color detection. The main purpose is to preliminarily identify pollutants in smoke and further develop smoke image detection technology. We discuss eight kinds of convolutional neural network, they can be used to classify smoke images. Testing different convolutional neural networks on this database, the accuracy of several existing networks is analyzed and compared, and the reliability of the database is also verified. Finally, the possible development direction of smoke detection is summarized.
This work is supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mgbemene, C.A., Nnaji, C.C., Nwozor, C.: Industrialization and its backlash: focus on climate change and its consequences. J. Environ. Sci. Technol. 9(1), 301–316 (2016)
Tollefson, J., Weiss, K.R.: Nations approve historic global climate accord. Nature 528, 315–316 (2015)
Wang, T., et al.: Air quality during the 2008 Beijing Olympics : secondary pollutants and regional impact. Atmos. Chem. Phys. 10(16), 7603–7615 (2010)
Guan, W., Zheng, X., Chung, K., Zhong, N.: Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action. Lancet 388(10054), 1939–1951 (2016)
Yauk, C., et al.: Germ-line mutations, DNA damage, and global hypermethylation in mice exposed to particulate air pollution in an urban/industrial location. Proc. Natl. Acad. Sci. 105(2), 605–610 (2008)
Voulvoulis, N., Georges, K.: Industrial and agricultural sources and pathways of aquatic pollution. In: Impact of Water Pollution on Human Health and Environmental Sustainability, pp. 29–54 (2016)
Saha, N., Rahman, M.S., Ahmed, M.B., Zhou, J.L., Ngo, H.H., Guo, W.: Industrial metal pollution in water and probabilistic assessment of human health risk. J. Environ. Manage. 185, 70–78 (2017)
Landrigan, P.J.: Air pollution and health. Lancet Public Health 2(1), e4–e5 (2017)
Orru, H., et al.: Residents’ self-reported health effects and annoyance in relation to air pollution exposure in an industrial area in Eastern-Estonia. Int. J. Environ. Res. Public Health 15(2), 252 (2018)
Sagna, K., Amou, K.A., Boroze, T.T.E., Kassegne, D., Almeida, A., Napo, K.: Environmental pollution due to the operation of gasoline engines: exhaust gas law. Int. J. Oil Gas Coal Eng. 5(4), 39–43 (2017)
Ma, S., Jin, C., Chen, G., Yu, W., Zhu, S.: Research on desulfurization wastewater evaporation: present and future perspectives. Renew. Sustain. Energy Rev. 100(58), 1143–1151 (2016)
Hallquist, M., et al.: Photochemical smog in China: scientific challenges and implications for air-quality policies. Natl. Sci. Rev. 3(4), 401–403 (2016)
Aidaoui, L., Triantafyllou, A.G., Azzi, A., Garas, S.K., Matthaios, V.N.: Elevated stacks’ pollutants’ dispersion and its contributions to photochemical smog formation in a heavily industrialized area. Air Qual. Atmos. Health 8(2), 213–227 (2015)
Yue, G., Gu, K., Qiao, J.: Effective and effificient photo-based PM2.5 concentration estimation. IEEE Trans. Instrum. Meas. 68(10), 3962–3971 (2019)
Gu, K., Xia, Z., Qiao, J., Lin, W.: Recurrent air quality predictor based on meteorology-and pollution-related factors. IEEE Trans. Multimedia 14(9), 3946–3955 (2018)
Gu, K., Xia, Z., Qiao, J.: Stacked selective ensemble for PM2.5 forecast. IEEE Trans. Instrum. Meas. (2019)
Gu, K., Qiao, J., Li, X.: Highly efficient picture-based prediction of PM2.5 concentration. IEEE Trans. Industr. Electron. 66(4), 3176–3184 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, September 2014
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, June 2016
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9, June 2015
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258, July 2017
Huang, G., Liu, Z., Weinberger, K. Q., Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708, August 2016
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)
Gu, K., Xia, Z., Qiao, J., Lin, W.: Deep dual-channel neural network for image-based smoke detection. IEEE Trans. Multimedia (2019)
Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, April 2017
Yuan, F., Shi, J., Xia, X., Fang, Y., Fang, Z., Mei, T.: High-order local ternary patterns with locality preserving projection for smoke detection and image classifification. Inf. Sci. 372, 225–240 (2016)
Lin, G., Zhang, Y., Zhang, Q., Jia, Y., Xu, G., Wang, J.: Smoke detection in video sequences based on dynamic texture using volume local binary patterns. TIIS 11(11), 5522–5536 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826, December 2016
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from over-fifitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, H., Chen, J., Li, S., Gu, K., Wu, L. (2020). Smoke Detection Based on Image Analysis Technology. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-3341-9_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3340-2
Online ISBN: 978-981-15-3341-9
eBook Packages: Computer ScienceComputer Science (R0)