한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제38권6호
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  • Pages.573-581
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  • 2020
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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CCTV 영상으로부터 미세먼지 추정에서 학습영상조합, 기상변수 적용이 결과에 미치는 영향

Effect of the Learning Image Combinations and Weather Parameters in the PM Estimation from CCTV Images

  • Won, Taeyeon (Dept. of Advanced Technology Fusion, Konkuk University) ;
  • Eo, Yang Dam (Dept. of Civil and Environmental Engineering, Konkuk University) ;
  • Sung, Hong ki (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Chong, Kyu soo (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Youn, Junhee (Dept. of Future Technology and Convergence Research, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2020.11.15
  • 심사 : 2020.12.21
  • 발행 : 2020.12.31
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초록

CCTV영상과 날씨 정보를 이용하여 미세먼지 농도를 추정하는 기법을 제안하고, 이에 대한 실험을 진행하였다. CCTV영상에 대해서는 특정 지점을 포함하는 일부 영역 영상과, 전체 영역 영상을 가지고 합성곱 신경망 (CNN)기반의 딥러닝 기법을 적용하여 PM 지수를 추정하는 방법을 제안하였다. 추가로 딥러닝에 의해서 예측된 결과 값을 습도 및 풍속 두 가지 날씨 특성과 결합한 뒤, 학습 된 회귀 모델을 사용하여 수정된 미세먼지 지수를 계산하는 후처리 실험도 함께 진행하였다. 실험 결과, CCTV영상으로부터 미세먼지 지수 추정 값은 R2가 0.58~0.89를 나타내었고, 측정기가 설치된 일부 영역 영상과 전체 영역 영상을 함께 학습시킨 결과가 가장 우수하였다. 기상변수를 이용한 후처리 적용결과는 실험지역의 모든 경우에 대하여 항상 정확도 향상을 보여주진 않았다.

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

키워드

참고문헌

  1. Awad, M. and Khanna, R. (2015), Support Vector Regression, Efficient Learning Machines, pp. 67-80.
  2. Behnke, S. (2003), Hierarchical Neural Networks for Image Interpretation, Lecture Notes in Computer Science, Draft submitted to Springer-Verlag. Vol. 2766
  3. Bo, Q., Yang, W., Rijal, N., Xie, Y., Feng, U., and Zhang, J. (2018), Particle Pollution Estimation from Images Using Convolutional Neural Network and Weather Features, IEEE International Conference on Image Processing (ICIP), Athens, pp.3433-3437
  4. Chao, Z., Yan, J., Li, C., Rui, X., Liu, L., and Bie, R. (2017), Image-based air quality analysis using deep convolutional neural network, MM '16: Proceedings of the 24th ACM international conference on Multimedia, pp. 297-301.
  5. Cortes, C. and Vapnik, V. (1995), Support-vector networks, Machine learning, 20, no.3, 273-297. https://doi.org/10.1007/BF00994018
  6. Harrison, R.M., Deacon, A.R., Jones, M.R., and Appleby, R.S. (1997), Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.), Atmospheric Environment, Volume 31, Issues 24, December, pp. 4103-4117. https://doi.org/10.1016/S1352-2310(97)00296-3
  7. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, and W., Jackel, L.D., (1989), Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, Volume 1, Issue 4, Dec., pp. 541 - 551. https://doi.org/10.1162/neco.1989.1.4.541
  8. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998), Gradient-based learning applied to document recognition. Proceedings of the IEEE, Vol. 86, No.11, pp. 2278-2324. https://doi.org/10.1109/5.726791
  9. Li, Y., Huang, J., and Luo, J. (2015), Using user generated online photos to estimate and monitor air pollution in major cities, Computer Vision and Pattern Recognition, arXi, https://arxiv.org/abs/1508.05028
  10. Liu, Ch., Tsow, F., Zou, Y., and Tao, N. (2016), Particle pollution estimation based on image analysis, PloS one, 11, no.2
  11. Lou, C., Liu, H., Li, Y., Peng, Y., Wang, J., and Dai, L. (2017), Relationships of relative humidity with PM 2.5 and PM 10 in the Yangtze River Delta, China, Environ Monit Assess, 2017 Oct 23;189(11):582, doi:10.1007/s10661-017-6281-z
  12. Mao, J., Phommasak, U., Watanabe, S. and Shioya, H. (2014), Detecting foggy images and estimating the haze degree factor, Journal of Computer Science & Systems Biology, 7:6
  13. Chakma, A., Vizena, B., Cao, T., Lin, J., and Zhang, J. (2017), On Estimating Air Pollution from Photos Using Convolutional Neural Network, IEEE International Conference on Image Processing (ICIP), pp.3949-3952, doi: 10.1109/ICIP.2017.8297023.
  14. Pope III, C.A., Ezzati, M., and Dockery D.W. (2009) Fine-Particulate Air Pollution and Life Expectancy in the United States, N Engl J Med, Volume 360, pp. 376-386. https://doi.org/10.1056/NEJMsa0805646
  15. Pope III, C.A. and Dockery D.W. (2012) Health Effects of Fine Particulate Air Pollution: Lines that Connect, Journal of the air & waste management association, Volume 56, Issues 6, pp. 709-742. https://doi.org/10.1080/10473289.2006.10464485
  16. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., and Fei, L.F. (2014), ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, Vol. 115, Issue. 3, pp. 211-252. https://doi.org/10.1007/s11263-015-0816-y
  17. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition, arXiv, https://arxiv.org/abs/1409.1556
  18. Song, A.R., and Kim, Y.I. (2017), Deep Learning-based Hyperspectral Image Classification with Application to Environmental Geographic Information Systems, Korean Journal of Remote Sensing, v. 33 no. 6 pt. 2, pp. 1061-1073. (in Korean with English abstract)
  19. Tai, A.P.K., Mickley, L.J., and Jacob, D.J. (2010), Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change, Atmospheric Environment, 44, 32, pp. 3976-3984. https://doi.org/10.1016/j.atmosenv.2010.06.060
  20. Zhao, H., Zhang, W., Sun, H., and Xue, B. (2019), Embedded Deep Learning for Ship Detection and Recognition, Future Internet, 11(2), 53 https://doi.org/10.3390/fi11020053