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A Shallow ResNet with Layer Enhancement for Image-Based Particle Pollution Estimation

  • Wenwen Yang
  • Jun FengEmail author
  • Qirong BoEmail author
  • Yixuan Yang
  • Bo Jiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Airborne particle pollution especially matter with a diameter less than 2.5 μm (PM2.5) has become an increasingly serious problem and caused grave public health concerns. An easily and reliable accessible method to monitor the particles can greatly help raise public awareness and reduce harmful exposures. In this paper, we proposed a shallow ResNet with layer enhancement for PM2.5 Index Estimation, called PMIE. An inter-layer weights discrimination of convolutional neural networks method is proposed, providing a meaningful reference for CNN’s design. In addition, a new method for enhancing the effect of the convolution layer was first introduced and was applied under the guidance of the CNN inter-layer weights discrimination method we proposed. This shallow ResNet consists of seven residual blocks with last two layer enhancements. We assessed our method on two datasets collected from Shanghai City and Beijing City in China, and compared with the state-of-the-art. For Shanghai dataset, PMIE reduced RMSE by 11.8% and increased R-squared by 4.8%. For Beijing dataset, RMSE is reduced by 14.4% and R-squared is increased by 23.6%. The results demonstrated that the proposed method PMIE outperforming the state-of-the-art for PM2.5 estimation.

Keywords

Particulate matter Image enhancement Shallow ReNet Layer enhancement 

Notes

Acknowledgement

The work in this paper is support by National Natural Science Foundation of China (No. 41601353), Shaanxi Provincial Natural Science Research Project (2017KW-010) and Shaanxi Provincial Department of Education Science Research Project (15JK1689).

References

  1. 1.
    Chow, J., et al.: Health effects of fine particulate air pollution: lines that connect. Air Repair 56(6), 709 (2006)Google Scholar
  2. 2.
    Mcginnis, J.M., Foege, W.H.: Actual causes of death in the United States. JAMA, J. Am. Med. Assoc. 291(10), 1238–1245 (1993)Google Scholar
  3. 3.
    Li, Y., Huang, J., Luo, J.: Using user generated online photos to estimate and monitor air pollution in major cities. In: International Conference on Internet Multimedia Computing and Service (2015)Google Scholar
  4. 4.
    Mao, J.: Detecting foggy images and estimating the haze degree factor. J. Comput. Sci. Syst. Biol. 7(6), 1 (2014)CrossRefGoogle Scholar
  5. 5.
    Liu, C., et al.: Particle pollution estimation based on image analysis. PLoS ONE 11(2), e0145955 (2016)CrossRefGoogle Scholar
  6. 6.
    Zhang, C., et al.: On estimating air pollution from photos using convolutional neural network. In: ACM on Multimedia Conference (2016)Google Scholar
  7. 7.
    Chakma, A., Vizena, B., Cao, T., Lin, J., Zhang, J.: Image-based air quality analysis using deep convolutional neural network. In: IEEE International Conference on Image Processing (2017)Google Scholar
  8. 8.
    Bo, Q., Yang, W., Rijal, N., Xie, Y., Feng, J., Zhang, J.: Particle pollution estimation from images using convolutional neural network and weather feature. In: IEEE International Conference on Image Processing (2018)Google Scholar
  9. 9.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2014)CrossRefGoogle Scholar
  10. 10.
    Szegedy, C., et al.: Rethinking the inception architecture for computer vision, pp. 2818–2826 (2015). Computer ScienceGoogle Scholar
  11. 11.
    He, K., et al.: Deep residual learning for image recognition, pp. 770–778 (2015)Google Scholar
  12. 12.
    Andrychowicz, M., Kurach, K.: Learning efficient algorithms with hierarchical attentive memory (2016)Google Scholar
  13. 13.
    Vaswani, A., et al.: Attention is all you need (2017)Google Scholar
  14. 14.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Northwest UniversityXianChina

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