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Environmental Science and Pollution Research

, Volume 26, Issue 3, pp 2722–2733 | Cite as

Camera sensor-based contamination detection for water environment monitoring

  • Yong WangEmail author
  • Xufan Zhang
  • Jun Chen
  • Zhuo Cheng
  • Dianhong Wang
Research Article
  • 42 Downloads

Abstract

Water environment monitoring is of great importance to human health, ecosystem sustainability, and water transport. Unlike traditional water quality monitoring problems, this paper focuses on visual perception of water environment. We first introduce the development of a customized aquatic sensor node equipped with an embedded camera sensor. Based on this platform, we present an efficient and holistic contamination detection approach, which can automatically adapt to the detection of floating debris in dynamic waters or the identification of salient regions in static waters. Our approach is specifically designed based on compressed sensing theory to give full consideration to the unique challenges in water environment and the resource constraints on sensor nodes. Both laboratory and field experiments demonstrate the proposed method can fast and accurately detect various types of water pollutants and is a better choice for camera sensor-based water environment monitoring compared with other methods.

Keywords

Environmental monitoring Camera sensor Contamination detection Compressed sensing 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61771436.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

References

  1. Achanta R, Süsstrunk S (2010) Saliency detection using maximum symmetric surround. 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, pp 2653–2656Google Scholar
  2. Achanta R, Hemami S, Estrada F, Süsstrunk S(2009) Frequency-tuned salient region detection. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami Beach, Florida, USA, pp 1597–1604Google Scholar
  3. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRefGoogle Scholar
  4. Adu-Manu KS, Tapparello C, Heinzelman W, Katsriku FA, Abdulai JD(2017) Water quality monitoring using wireless sensor networks: current trends and future research directions. ACM Trans Sens Netw 13(1):Article 4, 41 pagesGoogle Scholar
  5. Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724CrossRefGoogle Scholar
  6. Benezeth Y, Jodoin P, Emile B, Laurent H, Rosenberger C(2008) Review and evaluation of commonly implemented background subtraction algorithms. 19th International Conference on Pattern Recognition (ICPR’08), IEEE, Tampa, FL, USA, pp 1–4Google Scholar
  7. Borji A, Cheng MM, Jiang HZ, Jia L (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24(12):5706–5722CrossRefGoogle Scholar
  8. Cai F, Lu W, Shi W, He S (2017) A mobile device-based imaging spectrometer for environmental monitoring by attaching a lightweight, small module to a commercial digital camera. Sci Rep 7(15602):1–9Google Scholar
  9. Candes E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509CrossRefGoogle Scholar
  10. Capella JV, Bonastre A, Ors R, Peris M (2013) In line river monitoring of nitrate concentration by means of a wireless sensor network with energy harvesting. Sensors Actuators B Chem 177(1):419–427CrossRefGoogle Scholar
  11. Cheng MM, Mitra NJ, Huang XL, Torr PHS, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582CrossRefGoogle Scholar
  12. Corke P, Wark T, Jurdak R, Hu W, Valencia P, Moore D (2010) Environmental wireless sensor networks. Proc IEEE 98(11):1903–1917CrossRefGoogle Scholar
  13. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306CrossRefGoogle Scholar
  14. Dunbabin M, Marques L (2012) Robots for environmental monitoring: significant advancements and applications. IEEE Robot Autom Mag 19(1):24–39CrossRefGoogle Scholar
  15. Elhabian S, El-Sayed K, Ahmed S (2008) Moving object detection in spatial domain using background removal techniques—state-of-art. Recent Pat Comput Sci 1(1):32–54CrossRefGoogle Scholar
  16. Gan, L.(2007) Block compressed sensing of natural images. 15th International Conference on Digital Signal Processing, Cardiff, UK, pp. 403–406Google Scholar
  17. Goddijn-Murphy L, Dailloux D, White M, Bowers D (2009) Fundamentals of in situ digital camera methodology for water quality monitoring of coast and ocean. Sensors 9(7):5825–5843CrossRefGoogle Scholar
  18. Hall J, Zaffiro AD, Marx RB, Kefauver PC, Krishnan ER, Haught RC, Herrmann JG (2007) On-line water quality parameters as indicators of distribution system contamination. J Am Water Works Assoc 99(1):66–77CrossRefGoogle Scholar
  19. Højris B, Sarah C, Albrechtsen H, Smith C, Dahlqvist M (2016) A novel, optical, on-line bacteria sensor for monitoring drinking water quality. Sci Rep 6(23935):1–10Google Scholar
  20. Hossain A, Canning J, Ast S, Rutledge PJ, Jamalipour A(2014) Intelligent smartphone-based portable network diagnostics for water security case study realtime pH mapping of tap water. Eprint Arxiv arXiv:1408.0868Google Scholar
  21. Lambrou TP, Anastasiou CC, Panayiotou CG, Polycarpou MM (2014) A low-cost sensor network for real-time monitoring and contamination detection in drinking water distribution systems. IEEE Sensors J 14(8):2765–2772CrossRefGoogle Scholar
  22. Laut J, Henry E, Nov O, Porfiri M (2014) Development of a mechatronics-based citizen science platform for aquatic environmental monitoring. IEEE/ASME Trans Mechatron 19(5):1541–1551CrossRefGoogle Scholar
  23. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1999), IEEE, Fort Collins, CO, USA, pp 246–252Google Scholar
  24. Tavli B, Bicakci K, Zilan R, Barcelo-Ordinas JM (2012) A survey of visual sensor network platforms. Multimed Tools Appl 60(3):689–726CrossRefGoogle Scholar
  25. Toivanen T, Koponen S, Kotovirta V, Molinier M, Peng C (2013) Water quality analysis using an inexpensive device and a mobile phone. Environ Syst Res 2(9):1–6Google Scholar
  26. Wang Y, Wang DH (2014) Foreground extraction based on anomaly detection. Electron Lett 50(8):593–595CrossRefGoogle Scholar
  27. Wang Y, Tan R, Xing GL, Wang JX, Tan XB, Liu XM (2014) Aquatic debris monitoring using smartphone-based robotic sensors. Proceedings of the 13th International Symposium on Information Processing in Sensor Networks (IPSN’14), IEEE, Berlin, Germany, pp 13–24Google Scholar
  28. Wang Y, Wang DH, Lu Q, Luo DP, Fang W (2015) Aquatic debris detection using embedded camera sensors. Sensors 15(2):3116–3137CrossRefGoogle Scholar
  29. Xu G, Shen W, Wang X (2014) Applications of wireless sensor networks in marine environment monitoring: a survey. Sensors 14(9):16932–16954CrossRefGoogle Scholar
  30. Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, USA, pp 2814–2821Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Engineering and Electronic InformationChina University of GeosciencesWuhanChina
  2. 2.School of AutomationChina University of GeosciencesWuhanChina

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