  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.
