A computer vision system to localize and classify wastes on the streets

Mohammad Saeed Rad; Andreas von Kaenel; Andre Droux; Francois Tieche; Nabil Ouerhani; Hazım Kemal Ekenel; Jean Philippe Thiran

doi:10.1007/978-3-319-68345-4_18

A computer vision system to localize and classify wastes on the streets

Mohammad Saeed Rad^*, Andreas von Kaenel, Andre Droux, Francois Tieche, Nabil Ouerhani, Hazım Kemal Ekenel, Jean Philippe Thiran

^*Corresponding author for this work

Department of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

59 Citations (Scopus)

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.

Original language	English
Title of host publication	Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers
Editors	Markus Vincze, Haoyao Chen, Ming Liu
Publisher	Springer Verlag
Pages	195-204
Number of pages	10
ISBN (Print)	9783319683447
DOIs	https://doi.org/10.1007/978-3-319-68345-4_18
Publication status	Published - 2017
Event	11th International Conference on Computer Vision Systems, ICVS 2017 - Shenzhen, China Duration: 10 Jul 2017 → 13 Jul 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10528 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computer Vision Systems, ICVS 2017
Country/Territory	China
City	Shenzhen
Period	10/07/17 → 13/07/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

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10.1007/978-3-319-68345-4_18

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Rad, M. S., von Kaenel, A., Droux, A., Tieche, F., Ouerhani, N., Ekenel, H. K., & Thiran, J. P. (2017). A computer vision system to localize and classify wastes on the streets. In M. Vincze, H. Chen, & M. Liu (Eds.), Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers (pp. 195-204). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10528 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68345-4_18

Rad, Mohammad Saeed ; von Kaenel, Andreas ; Droux, Andre et al. / A computer vision system to localize and classify wastes on the streets. Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. editor / Markus Vincze ; Haoyao Chen ; Ming Liu. Springer Verlag, 2017. pp. 195-204 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A computer vision system to localize and classify wastes on the streets",

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.",

author = "Rad, {Mohammad Saeed} and {von Kaenel}, Andreas and Andre Droux and Francois Tieche and Nabil Ouerhani and Ekenel, {Hazım Kemal} and Thiran, {Jean Philippe}",

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Rad, MS, von Kaenel, A, Droux, A, Tieche, F, Ouerhani, N, Ekenel, HK & Thiran, JP 2017, A computer vision system to localize and classify wastes on the streets. in M Vincze, H Chen & M Liu (eds), Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10528 LNCS, Springer Verlag, pp. 195-204, 11th International Conference on Computer Vision Systems, ICVS 2017, Shenzhen, China, 10/07/17. https://doi.org/10.1007/978-3-319-68345-4_18

A computer vision system to localize and classify wastes on the streets. / Rad, Mohammad Saeed; von Kaenel, Andreas; Droux, Andre et al.
Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. ed. / Markus Vincze; Haoyao Chen; Ming Liu. Springer Verlag, 2017. p. 195-204 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10528 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Rad MS, von Kaenel A, Droux A, Tieche F, Ouerhani N, Ekenel HK et al. A computer vision system to localize and classify wastes on the streets. In Vincze M, Chen H, Liu M, editors, Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. Springer Verlag. 2017. p. 195-204. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68345-4_18

A computer vision system to localize and classify wastes on the streets

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