Chemicals Pollution Level Detection Based on Image Gray Difference

Authors

  • Leyuan Zang Department of Economics, University of Warwick, Coventry, United Kingdom Author
  • Ruohan Wang Tianjin Haihe High School, Tianjin, China Author
  • Zhenwu Xu School of Minerals Processing and Bioengineering, Central South University, Changsha, China Author
  • Han Wang School of International Communication, Hebei Institute of Communications, Shijiazhuang, China Author
  • Lu Liu School of Business & Management, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia Author

DOI:

https://doi.org/10.71222/4fwct125

Keywords:

image gray difference, chemical pollution, level detection, error rate

Abstract

Due to the complexity of environmental changes in chemical plants, the concentration of some polluted gases is affected and decreased, which changes the pixel characteristics of chemical pollution, resulting in deviations in the grade detection results of existing chemical pollution level detection methods. Therefore, a method for detecting chemical pollution level based on image gray difference is presented. Video images of chemical pollution were extracted by inputting chemical pollution images and selecting chemical pollution characteristic parameters. The intensity of chemical pollution was calculated by image gray difference algorithm, and the gray pixel characteristics of chemical pollution image were classified to realize the detection of chemical pollution level. The experimental results show that the proposed method has more successful times in detecting chemical pollution level and less error rate in detecting results. It can effectively improve the shortcomings of existing chemical pollution level detection.

References

1. F. Li, L. Mao, Y. Jia, et al., "Distribution and risk assessment of trace metals in sediments from Yangtze River estuary and Hangzhou Bay, China," Environ. Sci. Pollut. Res., vol. 25, pp. 855–866, 2018, doi: 10.1007/s11356-017-0425-0

2. K. P. Singh, S. Gupta, and P. Rai, "Identifying pollution sources and predicting urban air quality using ensemble learning methods," Atmos. Environ., vol. 80, pp. 426–437, 2013, doi: 10.1016/j.atmosenv.2013.08.023

3. S. Yin, H. Liu, and Z. Duan, "Hourly PM2.5 concentration multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model," Digit. Signal Process., vol. 118, p. 103221, 2021, doi: 10.1016/j.dsp.2021.103221

4. M. Krestenitis, G. Orfanidis, K. Ioannidis, et al., "Oil spill identification from satellite images using deep neural networks," Remote Sens., vol. 11, no. 15, p. 1762, 2019, doi: 10.3390/rs11151762

5. H. Liu, H. Tian, K. Zhang, et al., "Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China," Sci. Total Environ., vol. 657, pp. 657–670, 2019, doi: 10.1016/j.scitotenv.2018.12.068

6. M. Qu, W. Li, C. Zhang, et al., "Assessing the pollution risk of soil Chromium based on loading capacity of paddy soil at a regional scale," Sci. Rep., vol. 5, no. 1, p. 18451, 2015, doi: 10.1038/srep18451

7. G. Luo, Z. Han, J. Xiong, et al., "Heavy metal pollution and ecological risk assessment of tailings in the Qinglong Dachang antimony mine, China," Environ. Sci. Pollut. Res., vol. 28, pp. 33491–33504, 2021, doi: 10.1007/s11356-021-12987-7

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Published

22 January 2025

Issue

Vol. 2 No. 1 (2025)

Section

Article

License

Copyright (c) 2025 Leyuan Zang, Ruohan Wang, Zhenwu Xu, Han Wang, Lu Liu (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Zang, L., Wang, R., Xu, Z., Wang, H., & Liu, L. (2025). Chemicals Pollution Level Detection Based on Image Gray Difference. Journal of Computer, Signal, and System Research, 2(1), 38-42. https://doi.org/10.71222/4fwct125