Smoke Detection Based on Image Analysis Technology

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.

[1]  Feiniu Yuan,et al.  A Deep Normalization and Convolutional Neural Network for Image Smoke Detection , 2017, IEEE Access.

[2]  Nikolaos Voulvoulis,et al.  Industrial and Agricultural Sources and Pathways of Aquatic Pollution , 2016 .

[3]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Gao Xu,et al.  Smoke detection in video sequences based on dynamic texture using volume local binary patterns , 2017, KSII Trans. Internet Inf. Syst..

[5]  J. Tollefson,et al.  Nations approve historic global climate accord , 2015, Nature.

[6]  Philip J Landrigan,et al.  Air pollution and health. , 2017, The Lancet. Public health.

[7]  Song Guo,et al.  Photochemical smog in China: scientific challenges and implications for air-quality policies , 2016 .

[8]  Lakhdar Aidaoui,et al.  Elevated stacks’ pollutants’ dispersion and its contributions to photochemical smog formation in a heavily industrialized area , 2014, Air Quality, Atmosphere & Health.

[9]  Ke Gu,et al.  Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors , 2018, IEEE Transactions on Industrial Informatics.

[10]  Weisi Lin,et al.  Deep Dual-Channel Neural Network for Image-Based Smoke Detection , 2020, IEEE Transactions on Multimedia.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[13]  Yu Weijing,et al.  Research on desulfurization wastewater evaporation: Present and future perspectives , 2016 .

[14]  Komi Apélété Amou,et al.  Environmental Pollution due to the Operation of Gasoline Engines: Exhaust Gas Law , 2017 .

[15]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  W. Guan,et al.  Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action , 2016, The Lancet.

[17]  Ke Gu,et al.  Highly Efficient Picture-Based Prediction of PM2.5 Concentration , 2019, IEEE Transactions on Industrial Electronics.

[18]  Tao Mei,et al.  High-order local ternary patterns with locality preserving projection for smoke detection and image classification , 2016, Inf. Sci..

[19]  Huu Hao Ngo,et al.  Industrial metal pollution in water and probabilistic assessment of human health risk. , 2017, Journal of environmental management.

[20]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Hans Orru,et al.  Residents’ Self-Reported Health Effects and Annoyance in Relation to Air Pollution Exposure in an Industrial Area in Eastern-Estonia , 2018, International journal of environmental research and public health.

[23]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Junfei Qiao,et al.  Stacked Selective Ensemble for PM2.5 Forecast , 2020, IEEE Transactions on Instrumentation and Measurement.

[25]  Ke Gu,et al.  Effective and Efficient Photo-Based PM2.5 Concentration Estimation , 2019, IEEE Transactions on Instrumentation and Measurement.

[26]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[27]  D. Blake,et al.  Air quality during the 2008 Beijing Olympics: secondary pollutants and regional impact , 2010 .

[28]  C. A. Mgbemene,et al.  Industrialization and its Backlash: Focus on Climate Change and its Consequences , 2016 .

[29]  George R. Douglas,et al.  Germ-line mutations, DNA damage, and global hypermethylation in mice exposed to particulate air pollution in an urban/industrial location , 2008, Proceedings of the National Academy of Sciences.