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 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/.
@article{arxiv.2106.02740,
title = {ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes},
author = {Dina Bashkirova and Mohamed Abdelfattah and Ziliang Zhu and James Akl and Fadi Alladkani and Ping Hu and Vitaly Ablavsky and Berk Calli and Sarah Adel Bargal and Kate Saenko},
journal= {arXiv preprint arXiv:2106.02740},
year = {2022}
}