Title
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
Abstract
Less than 35% of recyclable waste is being actually recycled in the US [2], 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 are 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. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. 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. We believe that ZeroWaste will 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 at http://ai.bu.edu/zerowaste/
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.02047
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Datasets and evaluation, Segmentation,grouping and shape analysis
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Dina Bashkirova100.34
Mohamed Abdelfattah200.34
Ziliang Zhu300.34
James Akl401.01
Fadi Alladkani501.01
Ping Hu6435.12
Vitaly Ablavsky700.34
Berk Çalli8343.38
sarah adel bargal9946.77
kate saenko104478202.48