Skip to main content
SpringerLink
Log in

An AI-Based Approach to Automatic Waste Sorting

  • Conference paper
  • First Online:
HCI International 2020 - Posters (HCII 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1224))

Included in the following conference series:

Abstract

One of the major problems facing our cities is the disposal of the huge amount of waste produced every day. A possible solution is represented by recycling. In this article, we propose a system for automatic recognition and extraction of materials from the unsorted waste, which takes advantage of Computer Vision and Machine Learning techniques. The system can classify the material of incoming objects and grasp, and insert them into proper bins. For the material classification phase, the system analyzes the information captured by a Near-Infrared (NIR) camera and an RGB camera. Experimental tests performed on real-world datasets show encouraging accuracy values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.theworldcounts.com/challenges/planet-earth/state-of-the-planet/world-waste-facts (Accessed: 31/03/2020).

  2. 2.

    https://www.nytimes.com/2018/05/29/climate/recycling-landfills-plastic-papers.html (Accessed: 31/03/2020).

  3. 3.

    https://techcrunch.com/2016/09/13/auto-trash-sorts-garbage-automatically-at-the-techcrunch-disrupt-hackathon/ (Accessed: 31/03/2020).

  4. 4.

    https://www.flickr.com/ (Accessed: 31/03/2020).

References

  1. Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., Sansonetti, G.: Context-aware movie recommendation based on signal processing and machine learning. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRa 2011, pp. 5–10. ACM, New York (2011)

    Google Scholar 

  2. Caldarelli, S., Feltoni Gurini, D., Micarelli, A., Sansonetti, G.: A signal-based approach to news recommendation. In: CEUR Workshop Proceedings, vol. 1618. CEUR-WS.org, Aachen (2016)

    Google Scholar 

  3. De Rosa, M.P., Micarelli, A., Sansonetti, G.: An integrated system for automatic face recognition. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 140–147. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_20

    Chapter  Google Scholar 

  4. Feltoni Gurini, D., Gasparetti, F., Micarelli, A., Sansonetti, G.: iSCUR: interest and sentiment-based community detection for user recommendation on Twitter. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 314–319. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08786-3_27

    Chapter  Google Scholar 

  5. Feltoni Gurini, D., Gasparetti, F., Micarelli, A., Sansonetti, G.: Enhancing social recommendation with sentiment communities. In: Wang, J., et al. (eds.) WISE 2015. LNCS, vol. 9419, pp. 308–315. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26187-4_28

    Chapter  Google Scholar 

  6. Fogli, A., Sansonetti, G.: Exploiting semantics for context-aware itinerary recommendation. Pers. Ubiquit. Comput. 23(2), 215–231 (2019). https://doi.org/10.1007/s00779-018-01189-7

    Article  Google Scholar 

  7. Gasparetti, F., Micarelli, A., Sansonetti, G.: Exploiting web browsing activities for user needs identification. In: International Conference on Computational Science and Computational Intelligence, vol. 2, pp. 86–89, March 2014

    Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)

    Google Scholar 

  9. Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a Bayesian framework for material recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 239–246, June 2010

    Google Scholar 

  10. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th International Conference on Computer Vision ICCV, Corfu, vol. 2, pp. 1150–1157. IEEE Computer Society, USA (1999)

    Google Scholar 

  11. Micarelli, A., Neri, A., Sansonetti, G.: A case-based approach to image recognition. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 443–454. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44527-7_38

    Chapter  Google Scholar 

  12. Mittal, G., Yagnik, K.B., Garg, M., Krishnan, N.C.: SpotGarbage: smartphone app to detect garbage using deep learning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, pp. 940–945. Association for Computing Machinery, New York (2016)

    Google Scholar 

  13. Onori, M., Micarelli, A., Sansonetti, G.: A comparative analysis of personality-based music recommender systems. In: CEUR Workshop Proceedings, vol. 1680, pp. 55–59. CEUR-WS.org, Aachen (2016)

    Google Scholar 

  14. Prosperi, M.C., Fanti, I., Ulivi, G., Micarelli, A., De Luca, A., Zazzi, M.: Robust supervised and unsupervised statistical learning for HIV type 1 coreceptor usage analysis. AIDS Res. Hum. Retroviruses 25(3), 305–314 (2009)

    Article  Google Scholar 

  15. Salamati, N., Fredembach, C., Süsstrunk, S.: Material classification using color and NIR images. In: Proceedings of 17th Color Imaging Conference (CIC) (2009)

    Google Scholar 

  16. Sansonetti, G., Gurini, D., Gasparetti, F., Micarelli, A.: Dynamic social recommendation. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, pp. 943–947 (2017)

    Google Scholar 

  17. Sansonetti, G.: Point of interest recommendation based on social and linked open data. Pers. Ubiquit. Comput. 23(2), 199–214 (2019). https://doi.org/10.1007/s00779-019-01218-z

    Article  Google Scholar 

  18. Sansonetti, G., Gasparetti, F., Micarelli, A., Cena, F., Gena, C.: Enhancing cultural recommendations through social and linked open data. User Model. User-Adap. Inter. 29(1), 121–159 (2019). https://doi.org/10.1007/s11257-019-09225-8

    Article  Google Scholar 

  19. Thung, G., Yang, M.: Classification of trash for recyclability status (2016)

    Google Scholar 

  20. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, New York (1998)

    MATH  Google Scholar 

  21. Zhang, S., Forssberg, E.: Intelligent liberation and classification of electronic scrap. Powder Technol. 105(1), 295–301 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IES s.r.l., Via Amedeo Nazzari, 3, 00042, Anzio, RM, Italy

    Elio Strollo

  2. Department of Engineering, Roma Tre University, Via della Vasca Navale, 79, 00146, Rome, Italy

    Giuseppe Sansonetti, Marta Cialdea Mayer, Carla Limongelli & Alessandro Micarelli

Authors
  1. Elio Strollo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuseppe Sansonetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Cialdea Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carla Limongelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Micarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppe Sansonetti .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Strollo, E., Sansonetti, G., Mayer, M.C., Limongelli, C., Micarelli, A. (2020). An AI-Based Approach to Automatic Waste Sorting. In: Stephanidis, C., Antona, M. (eds) HCI International 2020 - Posters. HCII 2020. Communications in Computer and Information Science, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-50726-8_86

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50726-8_86

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50725-1

  • Online ISBN: 978-3-030-50726-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation