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International Work-Conference on Artificial Neural Networks

IWANN 2019: Advances in Computational Intelligence pp 350–361Cite as

Waste Classification System Using Image Processing and Convolutional Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

Abstract

Image segmentation and classification is more and more being of interest for computer vision and machine learning researchers. Many systems on the rise need accurate and efficient segmentation and recognition mechanisms. This demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. The use of convolutional neural networks for the image classification and recognition allows building systems that enable automation in many industries. This article presents a system for classifying plastic waste, using convolutional neural networks. The problem of segregation of renewable waste is a big challenge for many countries around the world. Apart from segregating waste using human hands, there are several methods for automatic segregation. The article proposes a system for classifying waste with the following classes: polyethylene terephthalate, high-density polyethylene, polypropylene and polystyrene. The obtained results show that automatic waste classification, using image processing and artificial intelligence methods, allows building effective systems that operate in the real world.

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References

  1. Kumar, P., Sikder, P.S., Pal, N.: Biomass fuel cell based distributed generation system for Sagar Island. Bull. Pol. Acad. Sci.: Tech. Sci. 66(5), 665–674 (2018)

    Google Scholar 

  2. Kaczorek, T.: Responses of positive standard and fractional linear systems and electrical circuits with derivatives of their inputs. Bull. Pol. Acad. Sci.: Tech. Sci. 66(4), 419–426 (2018)

    Google Scholar 

  3. Gundupalli, S.P., Hait, S., Thakur, A.: A review on automated sorting of source-separated municipal solid waste for recycling. Waste Manag. 60, 56–74 (2017)

    Article  Google Scholar 

  4. Al-Salem, S.M., Lettieri, P., Baeyens, J.: Recycling and recovery routes of plastic solid waste (PSW): a review. Waste Manag. 29(10), 2625–2643 (2009)

    Article  Google Scholar 

  5. Richard, G.M., Mario, M., Javier, T., Susana, T.: Optimization of the recovery of plastics for recycling by density media separation cyclones. Resour. Conserv. Recycl. 55(4), 472–482 (2011)

    Article  Google Scholar 

  6. Yuan, H., Fu, S., Tan, W., He, J., Wu, K.: Study on the hydrocyclonic separation of waste plastics with different density. Waste Manag. 45, 108–111 (2015)

    Article  Google Scholar 

  7. De Jong, T.P.R., Dalmijn, W.L.: Improving jigging results of non-ferrous car scrap by application of an intermediate layer. Int. J. Miner. Process. 49(1), 59–72 (1997)

    Article  Google Scholar 

  8. Pita, F., Castilho, A.: Influence of shape and size of the particles on jigging separation of plastics mixture. Waste Manag. 48, 89–94 (2016)

    Article  Google Scholar 

  9. Li, J., Xu, Z., Zhou, Y.: Application of corona discharge and electrostatic force to separate metals and nonmetals from crushed particles of waste printed circuit boards. J. Electrostat. 65(4), 233–238 (2007)

    Article  Google Scholar 

  10. Vajna, B., et al.: Complex analysis of car shredder light fraction. Open Waste Manag. J. 2(53), 2–50 (2010)

    Google Scholar 

  11. Patachia, S., Moldovan, A., Tierean, M., Baltes, L.: Composition determination of the Romanian municipal plastics wastes. In: Proceeding of the 26th International Conference on Solid Waste Technology and Management (2011)

    Google Scholar 

  12. Wang, C.Q., Wang, H., Fu, J.G., Liu, Y.N.: Flotation separation of waste plastics for recycling a review. Waste Manag. 41, 28–38 (2015)

    Article  Google Scholar 

  13. De Jong, T.P.R., Dalmijn, W.L.: X-ray transmission imaging for process optimisation of solid resources. In: Proceedings R: 02 Congress (2002)

    Google Scholar 

  14. De Jong, T.P.R., Dalmijn, W.L., Kattentidt, H.U.R.: Dual energy X-ray transmission imaging for concentration and control of solids. In: Proceedings of IMPC-2003 XXII International Minerals Processing Conference, Cape Town (2003)

    Google Scholar 

  15. Brunner, S., Fomin, P., Kargel, C.: Automated sorting of polymer flakes: fluorescence labeling and development of a measurement system prototype. Waste Manag. 38, 49–60 (2015)

    Article  Google Scholar 

  16. Bezati, F., Massardier, V., Balcaen, J., Froelich, D.: A study on the dispersion, preparation, characterization and photo-degradation of polypropylene traced with rare earth oxides. Polym. Degrad. Stab. 96(1), 51–59 (2015)

    Article  Google Scholar 

  17. Bezati, F., Froelich, D., Massardier, V., Maris, E.: Addition of tracers into the polypropylene in view of automatic sorting of plastic wastes using X-ray fluorescence spectrometry. Waste Manag. 30(4), 591–596 (2010)

    Article  Google Scholar 

  18. Huang, J., Pretz, T., Bian, Z.: Intelligent solid waste processing using optical sensor based sorting technology. In: 3rd International Congress on Image and Signal Processing (CISP), vol. 4, pp. 1657–1661. IEEE (2010)

    Google Scholar 

  19. Kreindl, G.: Sorting of mixed commercial waste for material recycling. In: Proceeding of TAKAG 2011 Deutsch-Trkische Abfalltage, Suttgart (2011)

    Google Scholar 

  20. Pieber, S., Meirhofer, M., Ragossnig, A., Brooks, L., Pomberger, R., Curtis, A.: Advanced waste-splitting by sensor based sorting on the example of the MTPlant Oberlaa. In: Tagungsband zur 10, DepoTech Conference, pp. 695–698 (2010)

    Google Scholar 

  21. Picn, A., Ghita, O., Whelan, P.F., Iriondo, P.M.: Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data. IEEE Trans. Ind. Inform. 5(4), 483–494 (2009)

    Article  Google Scholar 

  22. Picn, A., Ghita, O., Bereciartua, A., Echazarra, J., Whelan, P.F., Iriondo, P.M.: Real-time hyperspectral processing for automatic nonferrous material sorting. J. Electron. Imaging. 21(1), 013018 (2012)

    Article  Google Scholar 

  23. Bircanoglu, C., Atay, M., Beser, F., Genc, O., Kizrak, M.A.: RecycleNet: intelligent waste sorting using deep neural networks (2018)

    Google Scholar 

  24. Kokoulin, A.N., Tur, A.I., Yuzhakov, A.A.: Convolutional neural networks application in plastic waste recognition and sorting. In: IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1094–1098 (2018)

    Google Scholar 

  25. Chu, Y., Huang, C., Xie, X., Tan, B., Kamal, S., Xiong, X.: Multilayer hybrid deep-learning method for waste classification and recycling. Comput. Intell. Neurosci. (2018)

    Google Scholar 

  26. Wang, M., Wang, Z., Li, J.: Convolutional neural network applies to face recognition in small and medium databases. In: 4th International Conference on Systems and Informatics, ICSAI 2017, pp. 1368–1372, January 2018

    Google Scholar 

  27. Gua, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)

    Article  Google Scholar 

  28. Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 3119–3127 (2015)

    Google Scholar 

  29. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. PP, 1–21 (2019)

    Article  Google Scholar 

  30. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2012)

    Article  Google Scholar 

  31. Bobulski, J., Piatkowski, J.: PET waste classification method and plastic waste database - WaDaBa. In: Choraś, M., Choraś, R. (eds.) IP&C 2017. Advances in Intelligent Systems and Computing, vol. 681, pp. 57–64. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68720-9_8

    Chapter  Google Scholar 

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Acknowledgements

The project financed under the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019–2022 project number 020/RID/2018/19, the amount of financing 12,000,000 PLN.

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Authors and Affiliations

  1. Institute of Computer and Information Sciences, Czestochowa University of Technology, Dabrowskiego Street 69, 42-201, Czestochowa, Poland

    Janusz Bobulski & Mariusz Kubanek

Authors
  1. Janusz Bobulski
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  2. Mariusz Kubanek
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Correspondence to Mariusz Kubanek .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Bobulski, J., Kubanek, M. (2019). Waste Classification System Using Image Processing and Convolutional Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20517-1

  • Online ISBN: 978-3-030-20518-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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