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

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 bench- marking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.


Editor(s):
Liu, Ming
Chen, Haoyao
Vincze, Markus
Published in:
Computer Vision Systems, 195-204
Presented at:
11th International Conference on Computer Vision Systems, ICVS 2017, Shenzhen, China, July 10-13, 2017
Year:
2017
Publisher:
Springer International Publishing
ISBN:
978-3-319-68344-7
Laboratories:




 Record created 2017-10-03, last modified 2018-01-28

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