ZeroWaste Dataset: Towards Automated Waste Recycling
- URL: http://arxiv.org/abs/2106.02740v1
- Date: Fri, 4 Jun 2021 22:17:09 GMT
- Title: ZeroWaste Dataset: Towards Automated Waste Recycling
- Authors: Dina Bashkirova, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu,
Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko
- Abstract summary: We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste.
This dataset contains over1800fully segmented video frames collected from a real waste sorting plant.
We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
- Score: 51.053682077915546
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Less than 35% of recyclable waste is being actually recycled in the US, which
leads to increased soil and sea pollution and is one of the major concerns of
environmental researchers as well as the common public. At the heart of the
problem is the inefficiencies of the waste sorting process (separating paper,
plastic, metal, glass, etc.) due to the extremely complex and cluttered nature
of the waste stream. Automated waste detection strategies have a great
potential to enable more efficient, reliable and safer waste sorting practices,
but the literature lacks comprehensive datasets and methodology for the
industrial waste sorting solutions. In this paper, we take a step towards
computer-aided waste detection and present the first in-the-wild
industrial-grade waste detection and segmentation dataset, ZeroWaste. This
dataset contains over1800fully segmented video frames collected from a real
waste sorting plant along with waste material labels for training and
evaluation of the segmentation methods, as well as over6000unlabeled frames
that can be further used for semi-supervised and self-supervised learning
techniques. ZeroWaste also provides frames of the conveyor belt before and
after the sorting process, comprising a novel setup that can be used for
weakly-supervised segmentation. We present baselines for fully-, semi- and
weakly-supervised segmentation methods. Our experimental results demonstrate
that state-of-the-art segmentation methods struggle to correctly detect and
classify target objects which suggests the challenging nature of our proposed
in-the-wild dataset. We believe that ZeroWastewill catalyze research in object
detection and semantic segmentation in extreme clutter as well as applications
in the recycling domain. Our project page can be found
athttp://ai.bu.edu/zerowaste/.
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