Skip to main content

A Computer Vision System to Localize and Classify Wastes on the Streets

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10528)

Abstract

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-68345-4_18
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-68345-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Notes

  1. 1.

    http://www.cleaneuropenetwork.eu/de/measuring-litter/aus/.

  2. 2.

    http://www.avpu.fr/pdf%20AVPU/formation%20grille%20IOP-2014.pdf.

  3. 3.

    http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/urban/survey2015_en.pdf.

  4. 4.

    https://www.microsoft.com/cognitive-services/en-us/bing-image-search-api.

  5. 5.

    http://www.bafu.admin.ch/publikationen/publikation/01604/index.html?lang=fr.

References

  1. Sermanet, P., Eigen, D., Zhang, X., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: ICLR (2014)

    Google Scholar 

  2. Mittal, G., Yagnik, K.B., Garg, M., Krishnan, N.: Spotgarbage: smartphone app to detect garbage using deep learning. In: UbiComp (2016)

    Google Scholar 

  3. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  4. Sudha, S., Vidhyalakshmi, M., Pavithra, K., et al.: An automatic classification method for environment. In: TIAR (2016)

    Google Scholar 

  5. Carlos, B.L.J., Alejandro, R., Manuel, E.: Automatic waste classification using computer vision as an application in colombian high schools. In: LACNEM (2015)

    Google Scholar 

  6. Sakr, G., Mokbel, M., Darwich, A.: Comparing deep learning and support vector machines for autonomous waste sorting. In: IMCIT (2016)

    Google Scholar 

  7. Stewart, R., Andriluka, M.: End-to-end people detection in crowded scenes. In: CVPR (2015)

    Google Scholar 

  8. Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. CoRR, abs/1409.4842 (2014)

    Google Scholar 

  9. Abadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/

  10. Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)

    Google Scholar 

  11. Jia, Y., Caffe, S., et al.: Convolutional architecture for fast feature embedding. arXiv:1408.5093’14

  12. Everingham, M., Van Gool, L., Williams, C.K.I., et al.: The pascal visual object classes (VOC) challenge. In: IJCV (2010)

    Google Scholar 

Download references

Acknowledgment

The authors would like to thank Mr. Niels Michel, Manager of Dialog & Service at City of Zurich for sharing his in-depth experience on cleanliness measurement thus significantly contributing to this project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Saeed Rad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Rad, M.S. et al. (2017). A Computer Vision System to Localize and Classify Wastes on the Streets. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68345-4_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68344-7

  • Online ISBN: 978-3-319-68345-4

  • eBook Packages: Computer ScienceComputer Science (R0)