Abstract:
Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on
their neighborhoods, this paper presents a novel smartphone
app, called SpotGarbage, which detects and coarsely segments
garbage regions in a user-clicked geo-tagged image. The app
utilizes the proposed deep architecture of fully convolutional
networks for detecting garbage in images. The model has
been trained on a newly introduced Garbage In Images (GINI)
dataset, achieving a mean accuracy of 87.69%. The paper also
proposes optimizations in the network architecture resulting
in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in
resource constrained smartphones.
