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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
Pita, F., Castilho, A.: Influence of shape and size of the particles on jigging separation of plastics mixture. Waste Manag. 48, 89–94 (2016)
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)
Vajna, B., et al.: Complex analysis of car shredder light fraction. Open Waste Manag. J. 2(53), 2–50 (2010)
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)
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)
De Jong, T.P.R., Dalmijn, W.L.: X-ray transmission imaging for process optimisation of solid resources. In: Proceedings R: 02 Congress (2002)
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)
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)
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)
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)
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)
Kreindl, G.: Sorting of mixed commercial waste for material recycling. In: Proceeding of TAKAG 2011 Deutsch-Trkische Abfalltage, Suttgart (2011)
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)
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)
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)
Bircanoglu, C., Atay, M., Beser, F., Genc, O., Kizrak, M.A.: RecycleNet: intelligent waste sorting using deep neural networks (2018)
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)
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)
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
Gua, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)
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)
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)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2012)
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-20518-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20517-1
Online ISBN: 978-3-030-20518-8
eBook Packages: Computer ScienceComputer Science (R0)