Towards Cleaner Environments by Automated Garbage Detection in Images
Date Issued
2020-09-24
Author(s)
Despotovski, Aleksandar
Despotovski, Filip
Lameski, Jane
Abstract
The environment protection is becoming, now more than
ever, a serious consideration of all government, non-government, and
industrial organizations. The problem of littering and garbage is severe,
particularly in developing countries. The problem of littering is that it
has a compounding effect, and unless the litter is reported and cleaned
right away, it tends to compound and become an even more significant
problem. To raise awareness of this problem and to allow a future automated solution, we propose developing a garbage detecting system for
detection and segmentation of garbage in images. For this reason, we use
deep semantic segmentation approach to train a garbage segmentation
model. Due to the small dataset for the task, we use transfer learning
of pre-trained model that is adjusted to this specific problem. For this
particular experiment, we also develop our own dataset to build segmentation models. In general, the deep semantic segmentation approaches
combined with transfer learning, give promising results. They show great
potential towards developing a garbage detection application that can be
used by the public services and by concerned citizens to report garbage
pollution problems in their communities.
ever, a serious consideration of all government, non-government, and
industrial organizations. The problem of littering and garbage is severe,
particularly in developing countries. The problem of littering is that it
has a compounding effect, and unless the litter is reported and cleaned
right away, it tends to compound and become an even more significant
problem. To raise awareness of this problem and to allow a future automated solution, we propose developing a garbage detecting system for
detection and segmentation of garbage in images. For this reason, we use
deep semantic segmentation approach to train a garbage segmentation
model. Due to the small dataset for the task, we use transfer learning
of pre-trained model that is adjusted to this specific problem. For this
particular experiment, we also develop our own dataset to build segmentation models. In general, the deep semantic segmentation approaches
combined with transfer learning, give promising results. They show great
potential towards developing a garbage detection application that can be
used by the public services and by concerned citizens to report garbage
pollution problems in their communities.
Subjects
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